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10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Tuesday, February 18
 

8:15am GMT

Registration with Networking Tea / Coffee & Cookies
Tuesday February 18, 2025 8:15am - 8:45am GMT
Tuesday February 18, 2025 8:15am - 8:45am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

8:45am GMT

Welcome Remarks By
Tuesday February 18, 2025 8:45am - 8:55am GMT
Speakers/Session Chair
avatar for Amit Joshi, Ph.D

Amit Joshi, Ph.D

Conference Chair ICICT & BIOCOM 2025 Director, Global Knowledge Research Foundation
Tuesday February 18, 2025 8:45am - 8:55am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

8:55am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 8:55am - 9:05am GMT
Speakers/Session Chair
avatar for R. Simon Sherratt, PhD

R. Simon Sherratt, PhD

Professor, University of Reading, United Kingdom
Tuesday February 18, 2025 8:55am - 9:05am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:05am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 9:05am - 9:15am GMT
Speakers/Session Chair
avatar for Xin-She Yang, PhD

Xin-She Yang, PhD

United Kingdom, Professor, Middlesex University London, United Kingdom
Tuesday February 18, 2025 9:05am - 9:15am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:15am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 9:15am - 9:25am GMT
Speakers/Session Chair
avatar for Nilanjan Dey, PhD

Nilanjan Dey, PhD

Professor, Techno International New Town, India
Tuesday February 18, 2025 9:15am - 9:25am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:25am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 9:25am - 9:35am GMT
Speakers/Session Chair
avatar for Mr. Aninda Bose

Mr. Aninda Bose

Executive Editor, Springer Nature Group, United Kingdom
Tuesday February 18, 2025 9:25am - 9:35am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:35am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 9:35am - 9:45am GMT
Speakers/Session Chair
avatar for Mukesh Kumar, Ph.D

Mukesh Kumar, Ph.D

Associate Professor, University of Cambridge, United Kingdom
Tuesday February 18, 2025 9:35am - 9:45am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:45am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 9:45am - 9:55am GMT
Speakers/Session Chair
avatar for Milan Tuba, Ph.D

Milan Tuba, Ph.D

Head - Artificial Intelligence Project, Singidunum University & Vice-Rector of Research at Sinergija University, Serbia
Tuesday February 18, 2025 9:45am - 9:55am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

9:55am GMT

Address By Special Guest & Keynote Speaker BIOCOM 2025
Tuesday February 18, 2025 9:55am - 10:05am GMT
Speakers/Session Chair
avatar for Harshada Joshi, Ph.D

Harshada Joshi, Ph.D

Associate Prof. & Course Director, Department of Biotechnology, Mohanlal Sukhadia University, India
Tuesday February 18, 2025 9:55am - 10:05am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:05am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 10:05am - 10:15am GMT
Speakers/Session Chair
avatar for Tatiana Kalganova, Ph.D

Tatiana Kalganova, Ph.D

Professor & Director - Artificial Intelligence, Brunel University London, United Kingdom
Tuesday February 18, 2025 10:05am - 10:15am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:15am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 10:15am - 10:25am GMT
Speakers/Session Chair
avatar for Pancham Shukla, Ph.D

Pancham Shukla, Ph.D

Associate Professor, Imperial College London, United Kingdom
Tuesday February 18, 2025 10:15am - 10:25am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:25am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 10:25am - 10:35am GMT
Speakers/Session Chair
avatar for Monomita Nandy, Ph.D

Monomita Nandy, Ph.D

Vice Dean International / Professor - Accounting and Finance, Brunel University London, United Kingdom
Tuesday February 18, 2025 10:25am - 10:35am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:35am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 10:35am - 10:45am GMT
Speakers/Session Chair
avatar for Bal Virdee, Ph.D

Bal Virdee, Ph.D

Senior Professor & Head, London Metropolitan University, United Kingdom
Tuesday February 18, 2025 10:35am - 10:45am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:45am GMT

Address By Special Guest and Speaker
Tuesday February 18, 2025 10:45am - 10:55am GMT
Speakers/Session Chair
avatar for Mufti Mahmud, Ph.D

Mufti Mahmud, Ph.D

Professor, King Fahd University of Petroleum and Minerals, Saudi Arabia
Tuesday February 18, 2025 10:45am - 10:55am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

10:55am GMT

Vote of Appreciation & Felicitation
Tuesday February 18, 2025 10:55am - 11:00am GMT
Tuesday February 18, 2025 10:55am - 11:00am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

11:00am GMT

Felicitations and Conference Group Photograph
Tuesday February 18, 2025 11:00am - 11:15am GMT
Tuesday February 18, 2025 11:00am - 11:15am GMT
Ludgate Suite America Square Conference Centre, London, United Kingdom

11:45am GMT

Applied Green Computational Intelligence based Solutions to Enhance Sustainability in Precision Farming
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Maryem MELHAOUI, Abderrahim BAJIT, Khalid BOUALI, Youness ZAHID, Hicham Essamri, Rachid EL BOUAYADI
Abstract - To assure sustainability and optimize production and resource management in the agricultural sector while reducing the impact on the environment, we are currently using precision agriculture, which is mainly based on advanced technologies, including IoT platforms. In this context, the efficiency and accuracy of these IoT platforms depends heavily on the performance of its electronic resources and the distribution of its nodes. In the literature, the architecture of IoT platforms often consists of static nodes designated for the collection and communication of data to the computing machine. This distribution can also be mobile using self-driving robots on which nodes are hosted. In this perspective, we have studied, designed and deployed an IoT platform with dynamic distribution of nodes that is the subject of discussion and analysis in this manuscript. The idea is based on the intelligent deployment of the node operating mode, namely: activated or deactivated. To achieve this idea, we used two types of intelligent learning: supervised learning and unsupervised learning that both use a battery of intelligent models capable of analyzing and predicting the voluntary or involuntary missing data within the IoT platform. To recover missing data, two types of predicting solutions are available either by taking advantage of the arrival of certain new PAYLOAD’s components, or by using features’ history. To accomplish this we deployed Random Forrest (RF) and LSTM models. The paper’s objective consists of an intelligent and sustainable green solution to optimize the resource management of a dynamic IoT platform dedicated to greenhouse supervision.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

11:45am GMT

Implementing Digital Transformation in Service Industry Areas: Technological Problems and Solutions
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Rositsa Doneva, Nevena Staevsky, Zhelyana Doneva, Silvia Gaftandzhieva
Abstract - When implementing advanced technologies, companies face many challenges that can be overcome with the right approaches and solutions. This study investigates common implementation problems and possible solutions in digital transformation project performance in service industries. The paper presents the results of the first stage of the study, which aims to clarify its theoretical and practical foundations, as well as to focus on the research topic and to define the problem statement that should be resolved further. Based on the analysis of the factors requiring the use of cloud solutions, known approaches and technological solutions for digital transformation, the scope of the research is refined by identifying the target parameters by three main components (researched areas of the service industry, types of technological solutions for digitalization and the corresponding stages of their implementation) on which the study will focus. The problem statement is defined according to those parameters – for the target stages in implementing the selected technological solutions for digital transformation in the identified service industry areas. The achievements of this first stage will allow solutions to be proposed and tested to overcome technological problems which companies and institutions during introducing contemporary digital technologies and cloud services in their operational and business activities.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

11:45am GMT

Institutional Approach from a Relational Perspective: an analysis of the interactions of the Pernambuco Innovation System (SPIn)
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Anderson Diego Farias da Silva
Abstract - The role of innovation in the current context has led several governments to develop public policies capable of influencing public management. In view of this, this study aims to understand to what extent the relational approach of institutional theory can contribute to understanding the organization of production systems in certain innovation environments. To this end, a qualitative case study was conducted through the investigation of the Pernambuco Innovation System (SPIn). Documentary research and interviews were adopted as the research method. Regarding the results, SPIn analyses were carried out based on three dimensions: regulations; organizations; and institutions. Finally, it is suggested that an alternative path be indicated for the viability of the new Science, Technology, and Innovation Strategy for Pernambuco (2023-2027), through the adoption of a regulated and short-term regional governance structure.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

11:45am GMT

Replica Placement Strategy (RPS) for Effective Network Usage in Cloud Environment
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Fazlina Mohd Ali, Rohaya Latip, Syahanim Mohd Salleh, Nurhizam Safie Mohd Satar, Azran Ahmad, Surya Sumarni Hussein
Abstract - The cloud environment is substantial for streamlined operations and improved efficiency in the current digital landscape. Nevertheless, any systems on cloud platforms ought to face vulnerability and are bound to have a risk of failures and data loss. To ensure business continuity is preserved in any circumstances, data replication emerges as one of the solutions to hinder disruptions in cloud environments. This study explores the significant role of data replications with a multi-objective strategy and summarizes contributions developed by researchers to ensure replica placement guarantees the enhancement of numerous performance metrics. The prominent challenge is developing a comprehensive data replication strategy with a minimal replication process yet guarantees efficient network usage. Inefficient network usage remains a crucial problem derived from inappropriate data center selection criteria in cloud replica placement strategies. Therefore, to alleviate the issue, this research proposed a Replica Placement Strategy (RPS) for Effective Network Usage in a Cloud Environment, which identified decisive factors when selecting a data center to place replica copies. The key factors are file merit, space merit, and connection merit. The CloudSim simulation tool was used to conduct a comprehensive experiment, and the results proved that the proposed RPS significantly enhanced effective network usage by 76% and 109% betterments on average, outperformed the other two similar research work strategies, respectively. The key contributions will be visible to researchers exploring more explicit strategies in advancing cloud replication for future research demands.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

11:45am GMT

SKY CONTROL: A novel concept for a vendor-agnostic multi-cloud framework to optimize cost control and risk management for small and medium-sized enterprises
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Christian Baun, Henry-Norbert Cocos, Martin Kappes
Abstract - Multi-cloud setups have become increasingly common in the industry, and adopting this method brings many opportunities for companies like vendor diversification, a selection of Best-of-Breed Services, and an increased resilience of the services used. However, this approach also brings challenges for the users, such as increased complexity of managing the services across vendors and increased vulnerability of the services. Another unsolved issue is the need for more transparency in running costs of using multiple services from many vendors and the compliance of the services with binding regulations. Our proposed framework, SKY CONTROL, will tackle those challenges and develop a comprehensive planning tool for complex, distributed IT infrastructures. With our innovative solution approach, we will conduct static and dynamic resource analyses of the resource inventory. In addition, a cost calculator for hybrid cloud users will be implemented, providing an aggregated cost overview of cloud and on-premise systems. At the same time, critical data requires an equally transparent option for risk management and information governance so that data and processes in hybrid infrastructures are always located on systems with an appropriate level of protection. Our solution marks the first concrete implementation of the innovative Sky computing concept for small-medium enterprises.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

11:45am GMT

STUDENT SATISFACTION WITH THE USE OF MALAYSIAN SIGN LANGUAGE LEARNING MOBILE APPLICATIONS
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Authors - Syar Meeze Mohd Rashid, Muhammad Najwan Mat Zali
Abstract - This study identifies the satisfaction levels associated with the use of mobile applications in learning Malaysian sign language. With the advancement of digital technology, various applications have provided accessible and interactive platforms that facilitate learning. This research involved a survey of 30 students enrolled in a Malaysian sign language course, utilizing descriptive analysis and the Statistical Package for the Social Sciences (SPSS) for data assessment. The findings indicate a high overall satisfaction score among participants, showing that students are pleased with using the application for learning Malaysian sign language. However, this satisfaction study faces challenges, including the diverse backgrounds of users, technology accessibility, and the need for long-term effectiveness assessment. Regular user feedback is essential for the continuous improvement of the application. In conclusion, while this application demonstrates a positive impact on student satisfaction, further investigation is needed into its usability to maximize the effectiveness of application-based learning in the long term.
Paper Presenters
Tuesday February 18, 2025 11:45am - 12:00pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

12:00pm GMT

A Matched Pilot Resource Allocation Scheme for Downlink OFDMA systems
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Krishnan B. Iyengar, Raghavendra Pal, Upena D. Dalal, Anupam Shukla
Abstract - Channel Estimation in OFDMA communication systems requires transmission of pilot symbols. For a frequency selective fading channel, multiple pilots are needed in a single OFDM frame for the receivers/users to perform channel estimation. Comb-type pilots are a commonly used pilot allocation technique. However, such estimation techniques may have high computational complexity. In this work, we propose a Matched Pilot Resource Allocation approach which concentrates allocation of pilot subcarriers to particular users, to reduce the computational complexity at each of the receivers. In particular, we show that the proposed pilot assignment technique’s reduction in complexity improves as the number of channel taps increases. We also show that the proposed technique’s complexity reduction scales well in NOMA-OFDMA systems, where multiple users’ information may be multiplexed on a single sub-carrier.
Paper Presenters
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

12:00pm GMT

Agricultural Carbon Emission Prediction using Generalised Multi-Crop Machine Learning Models
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Raz Ehsani, Lakshmi Babu-Saheer
Abstract - This study explores the applicability of generalised machine learning models for predicting carbon emissions across multiple crops at a single location in Iran. Thirteen agricultural crops including alfalfa, beans, cabbage, carrots, corn, cucumbers, irrigated barley, irrigated wheat, lentils, onions, sugar beets, tomatoes, and watermelons are investigated. To enhance the dataset, Conditional Tabular Generative Adversarial Network (CTGAN) data augmentation approach is performed, generating 1,000 sample points for each crop. Various machine learning models, including K-Nearest Neighbours (KNN), Random Forest (RF), LASSO Regression, Multiple Linear Regression (MLR), Neural Networks (NNR), and a novel hybrid model; Recursive Feature Elimination with Heuristic Nearest Regression (RFE-HNR), are employed individually for each crop and collectively in a combined data model. The crops are categorised to facilitate carbon factor prediction model for category of crops. Results are compared with baseline cool farm Life Cycle Assessment tool. RF models consistently performed better on different combinations of datasets. This work provides valuable insights into the performance of diverse models in managing complex agricultural datasets and underscores the potential of data-driven approaches in optimising emissions, thereby contributing to sustainable agricultural practices.
Paper Presenters
avatar for Raz Ehsani

Raz Ehsani

United Kingdom
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

12:00pm GMT

Improvement of Replicas Placement in DFS
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Ladislav Pesicka, Lubos Matejka
Abstract - Distributed File Systems (DFS) have been used to store and access data for many years. Recently, with the increase in the number of mobile devices and their connection speeds using 5G, there is a need to optimize the structure of DFS to meet these demands. By placing data replicas appropriately, the performance of the DFS system can be improved. This paper discusses the effect of replica placement on improving DFS parameters.
Paper Presenters
avatar for Ladislav Pesicka

Ladislav Pesicka

Czech Republic
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

12:00pm GMT

Improving Machine Learning-based Activity Type Prediction from Time-Series EEG Data
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Syed Muhammad Raza Abidi, Tomas Emmanuel Ward, David C. Henshall, Gabriel-Miro Muntean
Abstract - Machine learning is essential to the development of personalized medicine, brain-computer interfaces (BCIs), classification and prediction, and the detection and elimination of artifacts in EEG signal data, among other applications. This work, in order to differentiate between target and non-target rapid serial visual presentation (RSVP) experimental conditions predicts the spatiotemporal patterns of entire trial types. We developed an optimized pipeline to preprocess EEG time-series data in a way that maximizes the relevance of event-related potentials (ERPs). We then utilized the machine learning techniques with the open-source EEG software, namely the MNE-Python tools (library), using the performance criteria, area under the receiver operating characteristic curve (ROC-AUC) with 5-fold cross-validation to predict the trial types.
Paper Presenters
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

12:00pm GMT

Predicting Students Academic Performance Based on Self-Cognitive Factors Using Machine Learning Algorithms
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Stephen Opoku Oppong, Benjamin Ghansah, Christopher Yarkwah, Einstein Kow Essibu, Winston Kwamina Essibu
Abstract - A significant problem in Educational Data Mining (EDM) receiving increasing attention is its ability to predict learners' academic performance. The ability to do this largely depends on the availability of datasets to train models. Conventionally, learners who are likely to fail a particular subject are identified through formative assessments and assisted by facilitators through guidance sessions and the implementation of interventions to help them optimise learning paths. Teachers also recommend personalised learning resources intended to build learners' capacity and understanding of the subject, which also hangs on the availability of data. In this study, Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) are used to generate synthetic data to augment existing data to help predict student performance using self-cognitive variables. The purpose was to balance the original dataset to avoid bias. The implementation was done using six machine learning classifiers (Naïve Bayes, Decision Tree, Extra Trees, K-Nearest Neighbor, Logistic Regression and Random Forest) to test which preprocessing method gives optimum performance. Extensive experimental results demonstrate that the GAN approach achieved a superior performance accuracy of 99.98%, significantly outperforming the SMOTE method, which gained 97.36%.
Paper Presenters
avatar for Einstein Essibu

Einstein Essibu

United States of America
avatar for Winston Essibu

Winston Essibu

United States of America
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

12:00pm GMT

Supervised Batch Normalization
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Authors - Bilal FAYE, Hanane AZZAG, Mustapha LEBBAH
Abstract - Batch Normalization (BN) enhances neural network generalization and accelerates training by normalizing mini-batches to a uniform mean and variance. However, its performance degrades with diverse data distributions. To overcome this, we introduce Supervised Batch Normalization (SBN), which extends normalization by leveraging multiple mean and variance parameters to account for contexts identified prior to training. These contexts—defined explicitly (e.g., domains in domain adaptation) or implicitly (e.g., via clustering algorithms)—ensure effective normalization for samples with shared features. Experiments across single-and multi-task datasets demonstrate the superiority of SBN over BN and other normalization techniques. For example, integrating SBN with Vision Transformer yields a 15.13% accuracy boost on CIFAR-100, while in domain adaptation scenarios, SBN with AdaMatch achieves a 22.25% accuracy gain on MNIST and SVHN compared to BN. Our code implementation is available on our GitHub repository: https://github.com/bfaye/ supervised-batch-normalization.
Paper Presenters
Tuesday February 18, 2025 12:00pm - 12:15pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

12:15pm GMT

Automatic Annotation of Clinicaltrials.gov Entities using Large Language Models
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Pouyan Nahed, Sepideh Farivar, Kazem Taghva
Abstract - This paper presents a large-scale biomedical Named Entity Recognition (NER) dataset automatically annotated using a Large Language Model (LLM) applied to the eligibility criteria from ClinicalTrials.gov. The dataset comprises over 4.6 million named entities, covering categories such as diseases, interventions, outcomes, and participants. A pseudo-labeling approach was employed to generate annotations with soft labels, providing confidence scores for each entity. We address challenges related to entity ambiguity and label inconsistency by introducing a structured mapping strategy to ensure uniformity across the dataset. The resulting dataset is a valuable resource for advancing tasks such as NER, information extraction, and text classification in biomedical research. By making this dataset publicly available, we aim to support the development of AI-driven healthcare applications.
Paper Presenters
avatar for Kazem Taghva

Kazem Taghva

United States of America
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

12:15pm GMT

Live Video Streaming from Helicopters to Ground via Terrestrial 5G and LTE Networks
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Shu Sum Law, Francis C.M. Lau
Abstract - Low latency and reliable connectivity are essential for live video streaming from helicopters in mission-critical scenarios, such as search and rescue missions and law enforcement activities. This paper addresses the challenges of achieving low latency and reliable connectivity for live video streaming from helicopters in mission-critical scenarios. We propose a location-based adaptive model leveraging bonded LTE and 5G cellular networks to dynamically adjust to varying cellular network conditions for aerial connection. Additionally, a packet-delay sliding-window (PDSW) channel model is presented for live video streaming from helicopters, along with a practical location-based adaptive Forward Error Correction (FEC) system to enhance error correction efficiency. Experimental results demonstrate the e effectiveness of the proposed approach in mitigating packet loss and ensuring reliable video streaming from helicopters.
Paper Presenters
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

12:15pm GMT

Navigating Market Volatility in Agriculture through Digital Analytics: Lessons from Indonesia's Coffee Sector
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Mohammad Hamsal, Faisal Binsar, Sri Bramantoro Abdinagoro, Gaguk Dwi Prasetyo Atmoko, Dani Rusli Utama
Abstract - Indonesia, as one of the world's largest coffee producers, faces significant market volatility that impacts the incomes of local coffee farmers. Despite rising global coffee prices, farmers often do not receive proportional benefits, raising important questions about the distribution of value in the sector and indicating the need for more innovative and robust approaches to market analysis. This study aims to analyze the discourse around Indonesian coffee in digital media and interpret the findings in the context of market dynamics and policy implications. The use of digital analytics and Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM) identified three dominant topics: (1) Coffee prices and economic stability, (2) Economic development and coffee industry growth, and (3) The role of farmers in the coffee industry growth. The analysis also shows a significant correlation between discussions about farmers and the economic aspects of the industry, underscoring the importance of economic and industry factors in farmers' lives. The implications of this study are highly relevant for stakeholders and policymakers, as the results can be used to design more synergistic policies that support industry growth while also addressing its direct impacts on farmers. The findings provide a data-driven basis for policy recommendations that can reduce market volatility and support the sustainability of the coffee industry.
Paper Presenters
avatar for Faisal Binsar

Faisal Binsar

Indonesia
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

12:15pm GMT

Optimizing Complex Variables for Gas Leak Detection and Grid Stability in Integrated Natural Gas and Green Hydrogen Power Systems
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Heictor Costa, Danielle Fortunato, Fernando Von Zuben, Denis Costa, Marcus Nunes
Abstract - This paper presents a novel approach to optimise complex variables for stabilising integrated power grids, focusing on the detection and mitigation of gas leaks in combined natural gas and green hydrogen systems. The research addresses the critical challenges of maintaining grid stability and reliability in the transition to renewable energy sources. By leveraging complex analysis in fluid mechanics, the study proposes an innovative method for gas leak detection using the complex potential and streamline distortion analysis. The methodology includes a genetic algorithm with multi-level optimization to enhance system stability while minimizing costs. The paper demonstrates the application of this approach on a 9-bus 8-node integrated grid, showcasing its potential to improve safety, reduce economic losses, and enhance the overall efficiency of power systems. This work was able to stabilise the integrated grid, as to maintain the same power and gas demand supply, by redirecting the gas route, decreasing the damage and cost caused by the gas leak, which promoted a economy of $14,000 dollars per active day of the grid.
Paper Presenters
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

12:15pm GMT

Political Security Threat Prediction with Transformer-Based Large Language Model
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Liyana Safra Zaabar, Noor Afiza Mat Razali, Sharifah Nabila S Azli Sham, Fazlina Mohd Ali
Abstract - The increasing spread of textual content on social media, driven by the rise of Large Language Models (LLMs), has highlighted the importance of sentiment analysis in detecting threats, racial abuse, violence, and implied warnings. The subtlety and ambiguity of language present challenges in developing effective frameworks for threat detection, particularly within the political security domain. While significant research has explored hate speech and offensive content, few studies focus on detecting threats using sentiment analysis in this context. Leveraging advancements in Natural Language Processing (NLP), this study employs the NRC Emotion Lexicon to label emotions in a political-domain social media dataset. The Bidirectional Encoder Representations from Transformers (BERT) model was applied to improve threat detection accuracy. The proposed model achieved an AUC value of 87%, with the BERT model achieving 91% accuracy, 90.5% precision, 81.3% recall and F1-score of 91%, outperforming baseline models. These findings demonstrate the effectiveness of sentiment and emotion-based features in improving threat detection accuracy, providing a robust framework for political security applications.
Paper Presenters
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

12:15pm GMT

Public Perception of ICT and Governance of Educational Curriculum Transformation: Analyzing Online Discussions in Indonesia
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Authors - Faisal Binsar, Stefanus, Muhammad Bayu, Abdul Razak, Gaguk Dwi Prasetyo Atmoko
Abstract - This study aims to evaluate public perceptions of implementing the Independent Curriculum in Indonesia, focusing on the positive and negative aspects that emerge in online discussions. Using a digital analytics approach, this study collects and analyzes data from various social media platforms to understand how the public responds to changes in the education system. Through topic analysis using the Latent Dirichlet Allocation (LDA) algorithm, this study successfully identified four main topics in positive and negative sentiments. In positive sentiments, the topics identified include independence and enthusiasm for learning, expectations for students, participation in education, and the development of independent curriculum and learning programs. In contrast, for negative sentiments, topics include dissatisfaction with the implementation of the policy, limitations in teaching and learning, teacher readiness and educational infrastructure, and the impact of the curriculum on education. These results indicate that despite high aspirations and some positive outcomes from implementing the Independent Curriculum, there are still significant challenges in its implementation that influence negative perceptions from the public. This study contributes to educational theory by emphasizing the importance of understanding public perception as an important factor in evaluating education policies. The implications of these findings suggest that policymakers need to consider input from the public to improve and adjust education policies more effectively.
Paper Presenters
avatar for Stefanus

Stefanus

Indonesia
Tuesday February 18, 2025 12:15pm - 12:30pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

12:30pm GMT

A New Optimized, Green Computational Intelligence, Secure, and Sustainable Solutions in Precision Farming
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Hicham ESSAMRI, Abderrahim BAJIT, Khalid BOUALI, Youness ZAHID, Rachid EL BOUAYADI
Abstract - The need for sustainable agricultural techniques has become a necessary need today, as the urgent need for management of vital agricultural resources such as water, energy and fertilizers has triggered the alarm signal and immediately imposed an urgent resolution on a global scale. The new technologies proposed by precision agriculture in agricultural greenhouses IOT platforms have offered innovative environments. The growth of hardware components inside its nodes and the outcomes on an intelligent platform show how processing power and energy efficiency have evolved over time. Future developments in intelligent agriculture are made possible by these strategies. Computational intelligence allows for energy sustainability, perceptual encoding reduces communication time, while visual watermarking optimizes data integrity protection. Moreover, data analysis becomes the main challenge in precision agriculture, because on the computing machine side of the agricultural greenhouse has become the centerpiece in terms of intelligent solicitation of data instead of a classic collection from the node sensors [1]. To intelligently collect the data when needed, this aim is reached on the one hand, between a node and its relative sensors, and on the other hand between the cloud and the IoT nodes. Optimizing such behavior on the cloud side requires a good understanding, modeling and validation based on a clear analysis of the data. Thanks to computational intelligence, we can approximate the results of lost and inaccurate sensors and generate the required forecasts using data analysis tools [2], thus ensuring that farmers make informed decisions and achieve better results. Methods that support objective and subjective optimization of PAYLOAD and have a notable impact on the overall greenhouse performance, such as data optimization, energy savings, and node state management. This objective is clearly ensured by successively deploying prediction of missing or unwanted data, visually based quality coding, energy efficiency in terms of idling any sleeping node, and synchronizing their handshake with Cloud. These features, as detailed later in this article, enhance precision farming performance.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

12:30pm GMT

A Two-Staged Intelligent Dynamic Video Summary based on Dictionary Learning and Multi-Objective Genetic Algorithm
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Aryan Kasare, Mohd Kaif Idrisi, Asif Iqbal, Dipti Jadhav
Abstract - Video Summarization (VS) is a methodology that highlights the main contents or events from the entire video in the form of keyframes. This paper presents a two-staged intelligent dynamic video summarization technique. In the first stage, SURF keypoints are extracted from the video frames and are given as input to a dictionary learning algorithm to identify the keyframes. In the second stage, the aim is to optimize the Stage-1 dynamic video summary using a multi-objective genetic algorithm to remove any redundancy in the generated video summary. The video summary results of the videos from the SUMME dataset demonstrate the validity of the proposed work. The subjective and objective performance analysis values justify the efficiency of the proposed algorithms to generate an optimized video summary.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

12:30pm GMT

CiBit: A Cryptocurrency for Academic Impact
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Nadav Voloch, Noa Voloch-Bloch, Guy Bostock, Shaked Pen Graitzer
Abstract - Cryptocurrencies have been an emerging venture for the past decade and a half. Their evaluations and amounts are increasing in the past couple of years. In this research we developed a cryptocurrency that aims to manifest academic reputation in a digital form. With the rise of mainstream cryptocurrency, CiBit aims to innovate and capitalize on the academic credentials system by integrating a new form of credit. Academic research is essential for building a reputation in academia, benefiting both researchers and their institutions. However, the true impact of research on academic society has remained largely unmonetized. CiBit Cryptocurrency seeks to address this gap by developing a non-mineable cryptocurrency where the generation of coins is directly linked to the number of citations and publications of academic articles. The objective of CiBit is to create a decentralized system where the authority over the currency is distributed among universities and academic institutions, which will act as banks and determine the currency's value. This decentralized approach ensures that no single entity has central control over the currency, promoting fairness and transparency in its valuation. Additionally, the CiBit platform includes a digital wallet feature, facilitating coin transactions between individuals and allowing withdrawals from academic institutions. By linking academic impact to a quantifiable and profitable currency, CiBit aims to provide a novel incentive for researchers and institutions, thereby enhancing the recognition and economic value of academic contributions.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

12:30pm GMT

Data Mining and Cybersecurity-Driven Solutions for CO2 Emissions Reduction of Different Maritime Shipping: A Multi-Faceted Analysis
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Saeed Rahimpour, Mahtab Shahin, Yigit Gulmez, Sanja Bauk
Abstract - Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO2 emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

12:30pm GMT

MultiModel and Multi-Step LSTM Model for Landslide Prediction
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Sahil Sankhyan, Praveen Kumar, Priyanka, K V Uday, Varun Dutt
Abstract - Landslides are a major natural hazard that can cause significant damage and loss of life. They are often triggered by heavy rainfall, earthquakes, or other factors that can destabilize the soil and rock. To mitigate risks associated with landslides, it is important to predict where and when they are likely to occur. In this study, we developed a multimodel and multistep LSTM (MML) model for landslide prediction. The models were trained on historical weather and soil property data from Kamand Valley, Himachal Pradesh, India. Kamand Valley is particularly susceptible to landslides, and the data collected in this study provides a feasible resource for developing and testing landslide prediction models. The MML model is a novel approach to landslide prediction. The model was composed of two LSTM layers and was trained to predict the weather and occurrence of landslides at multiple time steps in the future. LSTM layers were used to learn the long-term temporal dependencies in the data. The model was evaluated using a variety of metrics, including mean absolute error (MAE) and F1 score. The results showed that the model could achieve a high accuracy in predicting landslide occurrence. The final model is deployed in a web service, where it can be used to make real-time predictions. The web service is designed to be user friendly and easy to use, and can be accessed by anyone with an internet connection.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

12:30pm GMT

Spinnability Simulation using a Linear Combination of Deviatoric Stress and a Spring Connection among Particles
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Authors - Nobuhiko Mukai, Yansheng Fang, Youngha Chang
Abstract - One of the most challenging issues is visualizing liquid behavior based on physical simulation since the boundary is clear while its shape dynamically changes. It is simpler to represent Newtonian fluid since the shearing stress is proportional to its shearing velocity. On the other hand, a lot of non-Newtonian fluids are there. One of the non-Newtonian fluids is viscoelastic fluid, which has two properties: viscosity and elasticity. The viscoelastic fluid shows a unique behavior of “spinnability”. If it is stretched, the middle part becomes narrow. Then, it rapidly shrinks after the rupture. The behavior is so complex that the constitutive equation has not been established. Therefore, spinnability simulation has been performed by defining the deviatoric stress that is combined with viscosity and elasticity. This paper introduces two methods to represent spinnability. One is a combination of deviatoric stress that is combined linearly with viscous and elastic stresses, where the weight of elastic stress depends on the strain of the fluid, and the other is a spring connection among particles that construct the fluid. Finally, the relationship between the acceleration to be added for stretching and the elongation of the fluid has been found by comparing the simulation results with real viscoelastic fluid.
Paper Presenters
Tuesday February 18, 2025 12:30pm - 12:45pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

12:45pm GMT

A Novel Validation Metric of Green and Visually Optimized Integrity Protection of Precise Farming Plant’s Data
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Bouchra Essounaini, Abderrahim Bajit, Meryam Melhaoui, Hicham Essamri, Youness Zahid, Abdelhadi Allali, Rachid El Bouayadi
Abstract - The decline in the quality of agricultural products is a worrying phenomenon that results from several factors such as intensification of industrial agriculture, soil exhaustion, climate change, and transformation and storage practices. Awareness of these issues is crucial to reversing the trend and improving again the quality of agricultural products. This situation can lead to disruptions with varying impacts on the quality of agricultural products. This paper discusses an optimized method to improve information integrity in IOT agricultural platforms based on watermarking and blockchain technique. The purpose of this method is to hide a farming plant’s objective data in the subjective one and allows for the creation of a secure and transparent agricultural data ecosystem. A farming company uses IoT sensors. The sensor data is digitally tattooed and then recorded in a blockchain. The information can be accessed by regulators to verify that the crops comply with current standards, and consumers can view the complete production history of the product, ensuring its quality and authenticity. The combination of blockchain and watermarking in the agricultural sector provides a powerful framework to ensure the authenticity and integrity of data, guaranteeing its traceability while protecting it from any unauthorized manipulation, enhancing transparency, trust, and efficiency in the agricultural supply chain. This paper aims to optimize PAYLAOD integrity protection in IOT platforms by deploying an innovative and visually optimized watermarking coding that perceptually hides a plant’s objective data in the subjective one. This is done with a view to offering green, and secured solutions to agricultural greenhouse defense and human security. Based on the human visual system quality properties, our innovative solution downsizes the PAYLOAD’s size, improves its coding quality, reduces the energy consumption and time costs.
Paper Presenters
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

12:45pm GMT

Continual Learning at the Edge: An Agnostic IIoT Architecture
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Pablo Garcia-Santaclara, Bruno Fernandez-Castro, Rebeca P. Diaz-Redondo, Carlos Calvo-Moa, Henar Marino-Bodelon
Abstract - The exponential growth of Internet-connected devices has presented challenges to traditional centralized computing systems due to latency and bandwidth limitations. Edge computing has evolved to address these difficulties by bringing computations closer to the data source. Additionally, traditional machine learning algorithms are not suitable for the needs of edge-computing systems, where data usually arrives in a dynamic and continual way. However, incremental learning offers a good solution for these settings. Consequently, we introduce a new approach that applies the incremental learning philosophy within an edge-computing scenario for the industrial sector with a specific purpose: real time quality control in a manufacturing system. Applying incremental learning we reduce the impact of catastrophic forgetting and provide an efficient and effective solution.
Paper Presenters
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

12:45pm GMT

Digital Twin-Aided Contextual Bandit Learning based Two-Sided Matching for Task Offloading in Dynamic Fog Computing Networks
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Hoa Tran-Dang, Dong-Seong Kim
Abstract - This paper introduces a novel approach, Digital Twin-Aided Contextual Bandit Learning based Two-Sided Matching (DTCBL-TSM), to optimize computation offloading in dynamic fog computing networks (FCNs). Our proposed framework leverages digital twins (DTs) to enable precise monitoring and prediction of resource states of fog nodes (FNs). By integrating contextual bandit learning, our approach dynamically adapts to changing network conditions, learning optimal offloading strategies over time. Indeed, the two-sided matching mechanism ensures a balanced and fair allocation of tasks to fog nodes, considering both the task requirements and node capacities. Extensive simulations demonstrate that DTCBL-TSM outperforms existing methods in terms of task completion time, resource utilization, and adaptability to network dynamics.
Paper Presenters
avatar for Hoa Tran-Dang

Hoa Tran-Dang

South Korea
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

12:45pm GMT

Ethical Thinking in Cyber Resilience: Lessons from Malaysian Cyber Leaders
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Nurul Nuha Abdul Molok, Zahidah Zulkifli, Jongkil Jay Jeong, Sean Maynard, Atif Ahmad
Abstract - This study explores the intersection of ethical thinking and cyber resilience within the context of Malaysian government agencies. Given the increasing reliance on digital infrastructure and the rising threat of cyber-attacks, the need for robust cyber resilience strategies developed by people who are imbued with ethical values, is more critical than ever. However, the role of ethical considerations in shaping these strategies remains underexplored, especially in the public sector. Through a qualitative approach employing interviews as the primary data collection method and thematic analysis as the method for qualitative data analysis, this research proposes the importance of ethics in developing Malaysia’s cyber resilience. From the analysis, 4 themes have been identified: moral and spiritual beliefs, alignment to the government agenda, responsibility and long-term thinking. Findings suggest that ethical thinking plays a crucial role in decision-making processes which influence the development and implementation of cyber related strategies, policies, and law in Malaysian public sector organisations. This study proposes the integration of ethics in the development of national cybersecurity strategies, to make the country more resilient to protect itself against cyber threats.
Paper Presenters
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

12:45pm GMT

Evaluating Obstacles to Industry 4.0 in Cambodia’s Garment Sector using Interpretive Structural Modeling
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Siphat Lim, Edman Padilla Flores
Abstract - The investigation into the challenges of adopting Industry 4.0 in Cambodia’s garment sector has identified stringent national policies as the primary obstacle to technological progress. Furthermore, the MICMAC analysis has high-lighted the presence of a singular autonomous variable, specifically the absence of cyber-physical systems. In Cluster II, the variables related to extended learning cycles, the lack of advancements in mechatronic systems, and limited agility within supply chains exhibited the most significant interconnections, with insufficient training closely following in importance. Notably, Cluster III lacked any interconnected variables. Among the numerous factors analyzed, the implementation of rigorous national policies demonstrated the most substantial impact, followed by a lack of dedication from senior management, inadequate financial resources, and shortcomings in information technology infrastructure, all of which were recognized as additional challenges. These challenges are classified as independent or driving factors, and addressing them, since they represent the fundamental causes, would naturally lead to the removal of obstacles present at higher levels.
Paper Presenters
avatar for Siphat Lim

Siphat Lim

Cambodia
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

12:45pm GMT

Unraveling Social Network Factors in Predicting Depression with a Machine Learning Approach
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Authors - Eunjae Kim, Kyu-man Han, Eun Kyong Shin
Abstract - This study identifies the key factor contributing to major de-pressive disorder using a machine learning approach. Depression is a global public health concern, particularly significant in South Korea due to its strong association with high suicide rates. While demographic, socioeconomic, medical history and social network-focused factors are associated with depression, the consensus on the most critical one is challenging due to methodological limitations. To address this, we applied Partial Least Squares Discriminant Analysis (PLS-DA) and evaluated selectivity ratios. 172 participants were included, 70 depressed and 102 non-depressed, assessed by the Hamilton Depression Rating Scale. To gauge the social embeddings of participants, we used UCLA Loneliness Scale (UCLA-3). We included demographic, socioeconomic, and medical history features for the all-inclusive model. We found that the social network related factors were more critical than others. Seven items from the UCLA, including “No one really knows me well”, had a selectivity ratio greater than 2. No features from other factors were found significant. This study underscores that poor-quality social relationships are strongly associated with depression. These findings can enhance early screening for depression and enable the development of tailored interventions for effective treatment and management.
Paper Presenters
avatar for Eun Kyong Shin

Eun Kyong Shin

Republic of Korea
Tuesday February 18, 2025 12:45pm - 1:00pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

1:00pm GMT

A New Green, Intelligent, and Autonomous Mobile Node’s Solutions to Precision Farming Sustainability
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Youness ZAHID, Abderrahim BAJIT, Hiba GAIZI, Hicham ESSAMRI, Rachid EL BOUAYADI
Abstract - The increasing need for sustainable agricultural practices stems from urgent global environmental concerns. Greenhouses offer controlled environments that enhance crop productivity by leveraging technological advancements. A promising solution to these challenges involves integrating smart greenhouses with mobile IoT nodes equipped with autonomous navigation capabilities. This paper presents an advanced mobile node capable of autonomously navigating a predefined path in the greenhouse, utilizing advanced computer vision and deep learning models. The node employs convolutional neural networks (CNNs) to accurately follow the path and pause at each plant, gathering comprehensive objective and subjective data, thereby enhancing traditional IoT functionalities. These IoT devices are critical for presenting data and establishing communication via synchronized wired and wireless systems. However, data security remains a persistent challenge. Through computational intelligence, the system compensates for lost or erroneous sensor data, delivering precise forecasts and enabling farmers to make data-driven decisions to enhance productivity. The mobile node not only collects data but also functions as an IoT hub, managing communication with various sensors and ensuring effective data exchange between IoT nodes and the cloud. This article outlines methods that optimize data analysis, predictive accuracy, and energy efficiency, significantly improving system performance. Our approach offers a solution that addresses sustainability while enhancing operational efficiency in smart greenhouses.
Paper Presenters
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Fleet Suite - 1D America Square Conference Centre, London, United Kingdom

1:00pm GMT

Design and Implementation of Ship Navigational Information System using Smartphone Sensor Data
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Ryota IMAI, Atsushi ISHIBASHI, Ayoung YANG, Tsuyoshi MIYASHITA, Tadasuke FURUYA
Abstract - To ensure safe navigation, navigators collect various types of navigational information, such as geographical conditions, movements of other ships, and weather and sea state during voyages. In recent years, services providing navigational information via the Internet have been developed. These services are especially useful for small ships with limited costs because they do not require specialized equipment such as AIS (Automatic Identification System). However, these services are inadequate for actual navigation because they cannot obtain information on non-AIS ships. In this research, we propose a ship navigational information system that utilizes smartphone sensor data. By using smartphones equipped with location services, gyroscopes, and acceleration sensors, even non-AIS ships can share their navigational information with surrounding ships. Furthermore, small ships that cannot be equipped with so many nautical instruments can obtain the geographical conditions, the movements of other ships, and the impact of weather and sea state on their own ships via a TCP/IP network. A prototype web application was developed and tested on a real ship to verify that it can provide users with navigational information.
Paper Presenters
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Ludgate Suite - 1F America Square Conference Centre, London, United Kingdom

1:00pm GMT

Meteorological Insights: Scalable Weather Pattern Mining in Tallinn and Tartu
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Mahtab Shahin, Tara Ghasempouri, Juan Aznar Poveda, Nasim Janatian, Thomas Fahringer, S. A. Shah, Dirk Draheim
Abstract - Accurate weather and climate prediction is essential for early warning systems that improve response strategies to climate-related events. This study explores the use of association rule mining (ARM) techniques to analyze large-scale meteorological datasets. We focus on the weather patterns of Tallinn and Tartu, investigating variables such as wind speed, temperature, precipitation, and humidity, and their influence on weather intensity. A distributed ARM approach is employed using the Apollo framework, which utilizes serverless functions to enhance scalability and performance. Results show Apollo outperforms traditional systems like Apache Spark by approximately 15% in terms of processing speed, while extracting a greater number of meaningful rules. Time-series analysis was also applied to investigate temporal weather trends. Our findings highlight the potential of this approach for enhancing weather prediction systems and offer a foundation for future research in this area.
Paper Presenters
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Newgate Suite - 1B America Square Conference Centre, London, United Kingdom

1:00pm GMT

Natural Language Processing Model for Promoting Performance of E-Business Enterprises
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Mohamed Elhoseny, Abdelaziz Darwiesh, A.H. ElBaz, Reem Atassi
Abstract - In this article, an NLP model is suggested to improve e-business performance by identifying and assessing the potential risks. Based on surveying the literature, we found that the customers' perceptions are not included in developing e-business enterprises. Moreover, a few articles include managing risks based on the indications of users. So, we propose a model which relies on the indications of social media platforms as a fashionable data repository. This model will help managers in building effective e-business enterprises. Moreover, Twitter's real data for the customers of Amazon Canada is collected and analyzed where the findings refer to the common risks from the point of customers' view are operational and technological risks. Besides, we provide many performance metrics to confirm the suggested methodology’s efficacy.
Paper Presenters
avatar for Mohamed Elhoseny

Mohamed Elhoseny

United Arab Emirates
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Bishopsgate Suite - 1A America Square Conference Centre, London, United Kingdom

1:00pm GMT

Safeguarding Asynchronicity of Lua’s Garbage Collector and Multithreading
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Melike Aysenur Yildirim, Fadi Yilmaz
Abstract - In contemporary cybersecurity, safeguarding systems from malicious attacks is paramount, given the proliferation of interconnected tools and the continuous efforts of cyber adversaries to disrupt these systems. Researchers have explored various methods to establish secure environments, including advanced techniques for detecting and mitigating cyber threats. One such method, in-lined reference monitoring (IRM), leverages a language-based security approach and has shown promise in enhancing system security. This paper focuses on the application of the IRM through the Lua programming language, renowned for its efficiency and widespread use in diverse domains such as gaming, scientific computing, and the Internet of Things (IoT) devices. We introduce and evaluate LuaLight, a novel, fully automated system designed to transform Lua executables to address use-after-free (UAF) vulnerabilities inherent in the Lua garbage collector (LGC), without necessitating modifications to the vulnerable Lua virtual machines (LVM). LuaLight offers a comprehensive solution to several known security vulnerabilities associated with the LGC, specifically addressing issues identified in CVE-2020-24371 and CVE-2021-44964.
Paper Presenters
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Walbrook Suite - 1C America Square Conference Centre, London, United Kingdom

1:00pm GMT

The Needs for Sustainability Elements and Technology Integration into The Future Fashion Design Curriculum
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Authors - Khairul Azhar Jamaludin, Nor’Aqilah Ahmad Zabidi
Abstract - The increasing need for the fashion industry to transition towards sustainability necessitates the ongoing integration of Education for Sustainable Development (ESD) and digital technology within the education sector and the industry. The fashion industry is widely recognized as a substantial contributor to various social and environmental issues, exerting adverse environmental effects throughout its production processes and consumer disposal practices. In light of pressing global concerns, it is imperative to incorporate sustainability components and digital technology into the fashion design curriculum offered by Technical and Vocational Education and Training (TVET) institutions. This measure is crucial to adequately equipping future students to meet the demands and requirements of the fashion industry. This concept paper explores the necessity of incorporating sustainability elements into the fashion design art curriculum, focusing on aspects of model and theory, current implementation, and integration requirements. Based on the findings, it is recommended that sustainability be incorporated into the fashion design curriculum offered at TVET institutions. Furthermore, this integration should encompass collaborative efforts with the industry, aligning with the requirements of the contemporary industrial revolution.
Paper Presenters
Tuesday February 18, 2025 1:00pm - 1:15pm GMT
Aldgate Suite - 1E America Square Conference Centre, London, United Kingdom

2:15pm GMT

Comparison of Bayesian and Frequentist Neural Networks on Air Quality Time Series Problems
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Authors - Christopher Sinclair, Saptarshi Das
Abstract - This paper aims to compare Bayesian and frequentist versions of Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) neural networks via cross validation and bench-marking by addressing a regression problem on a time series dataset using similar network structures. We compare the model performance using the coefficient of determination (R2) score, Pearson and Spearman correlation coefficients by varying size of the dense units in the recurrent layers in both deep learning models.
Paper Presenters
avatar for Christopher Sinclair

Christopher Sinclair

United Kingdom
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

2:15pm GMT

Forensic Voice Analysis: Neural Networks vs. Support Vector Machines for Speaker Identification
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Authors - Aditya Kurniawan, Budiman Wijaya, Hady Gustianto
Abstract - This study presents a comparative analysis of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for speaker classification tasks, focusing on their performance metrics, computational efficiency, and adaptability to diverse datasets. The research utilized public datasets and volunteer recordings to train models using x-vector embeddings extracted via the SpeechBrain encoder. As for models evaluation, the precision, recall, F1-score, and accuracy were used. Results show that both ANNs and SVMs achieve good robustness in dealing with class imbalance and the overall accuracy for the SVMs (97%) is only marginally better than that for ANNs (96%). Using parameter tuning, this study shows the computation speed of SVMs and flexibility of ANNs and provides some insight on how to choose models in the speaker classification applications.
Paper Presenters
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

2:15pm GMT

Promoting Active Learning and Autonomy in English as a Second Language (ESL) Classroom through iPads: An Applied Framework
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Authors - Harwati Hashim, Muhammad Saufi Firdaus Sabudin
Abstract - Technology integration into education has become increasingly crucial for fostering active learning and cultivating learner autonomy, particularly in the context of language acquisition. In the English as a Second Language (ESL) classroom, iPads offer dynamic possibilities for interactive learning, allowing students to engage more meaningfully with language content. However, despite the availability of such tools, many ESL educators continue to rely on traditional, teacher-centred approaches that limit opportunities for student independence. This paper introduces a framework for leveraging iPads to foster active learning and autonomy among ESL learners. The study's primary objective is to develop an applied framework that educators can use to enhance the effectiveness of iPad-based learning activities in ESL classrooms. The framework is based on a thorough review of existing literature on mobile-assisted language learning (MALL), active learning, and learner autonomy, identifying best practices and successful applications of iPads in educational settings. The findings indicate that integrating iPads can significantly boost students’ motivation, engagement, and self-directed learning. Specific strategies highlighted include project-based learning, digital storytelling, and collaborative activities that harness the interactive features of iPads. The study concludes that implementing this framework enables ESL educators to create more student-centred learning environments, equipping learners with the skills needed for both language acquisition and independent learning in a technology-driven world.
Paper Presenters
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

2:15pm GMT

Tale of Two Domains: Cyber-Physical
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Authors - Benjamin Lampe, Patience Yockey, Virginia Wright
Abstract - As devices and systems continue to modernize and adopt integrated circuits, the use of cyber technology to deploy an application is the expectation. This deployment through cyber assets brings new cyber-risk and cybersecurity is the practice of managing this risk. Cyber-risk is constantly changing due to the speed of technology advancement and the changing quality of the adversary. Cyber-Informed Engineering (CIE) mitigates cyber-risk through engineering controls whereas the traditional practice of cybersecurity mitigates cyber-risk through cybersecurity controls. By clearly defining the cyber-physical boundary, engineering controls and cybersecurity controls can clearly demonstrate their complementary nature to provide layered defenses and successfully mitigate cyber-risk through independent controls. In this paper, a layered model of device decomposition of the cyber-physical boundary is presented to provide clarity where engineering controls are used to reduce cyber-risk within the physics, functional materials, electronic, or integrated circuit layers and where cybersecurity controls are used to reduce cyber-risk within the machine code and application layers. By implementing both traditional cybersecurity controls and engineering controls, a more holistic approach to cybersecurity is achieved in protecting modern devices and systems, as well as a clear awareness in identifying, documenting, and authorizing the system’s cybersecurity protection scheme is achieved.
Paper Presenters
avatar for Benjamin Lampe

Benjamin Lampe

United States of America
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

2:15pm GMT

Towards Integrated Health Information Systems Implementation in Ghana Health Service
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Authors - Emmanuel Awuni Kolog, Sancho Ackoussah, Sulemana Bankuoru Egala
Abstract - The fragmented health information system currently operated in Ghana besets universal access to health data and service delivery resulting in the government’s efforts to integrate a coordinated healthcare information system. This study investigates the factors affecting the implementation of integrated national health information systems (IHIS) in Ghana, highlighting the role of digital curiosity among health workers. Analyzing data from 446 Ghana Health Service affiliates using structural equation modeling, key factors identified include task characteristics, organizational viability, and state policy. While economic viability and IT infrastructure were less influential, they may improve with greater government involvement. Digital curiosity significantly mediates the relationship between technological factors and IHIS implementation, suggesting the need to cultivate curiosity among health workers for better health outcomes.
Paper Presenters
Tuesday February 18, 2025 2:15pm - 2:30pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

2:30pm GMT

Balancing Local Production and Imports Using Mixed-Integer Programming: A Case Study of Qatar
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Authors - Raka Jovanovic, Sa’d Abdel-Halim Shannak, Antonio P. Sanfilippo
Abstract - This paper develops a linear programming model to optimize the balance between domestic agricultural production and imports in Qatar, aligning with the goals of the Qatar National Food Security Strategy (QNFSS). The model incorporates economic, environmental, and policy constraints to ensure food security while addressing critical challenges such as resource limitations, supply chain resilience, and reliance on imports. By evaluating various production and import scenarios, the study identifies strategies to enhance sustainability and reduce vulnerability to global market disruptions. Key findings highlight the importance of optimizing land and water use for local production while diversifying import sources to mitigate risks. The results also emphasize the role of government policies in achieving a sustainable balance between cost-effectiveness and strategic resilience. The proposed approach provides a decision-support framework adaptable to other resource-constrained, import-dependent nations. Future extensions could include dynamic modeling to capture evolving market conditions and nonlinear optimization techniques for more complex trade scenarios.
Paper Presenters
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

2:30pm GMT

Direction-of-Arrival Estimation Method for Multiple Sources Using Spatiotemporal Spectra with Sparse Bayesian Estimation
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Authors - Senta Ariizumi, Teruki Toya, Kenji Ozawa
Abstract - This paper presents a novel direction-of-arrival (DOA) estimation method for sound that utilizes sparse Bayesian estimation with a two-dimensional spatiotemporal spectrum as a transfer function. The proposed method is designed for compact microphone arrays, such as those embedded in smartphones, enabling the accurate estimation of multiple sound-source DOAs. This method leverages the sparsity of the spatiotemporal spectrum to enhance robustness and accuracy, addressing the limitations of conventional approaches based on steering vectors. Computational simulations were conducted to explore the optimal parameter settings of the method and evaluate its performance in comparison with that of a traditional method. The results showed that the proposed approach achieved higher accuracy and maintained robust performance in noisy environments. These findings highlight the potential of the method for real-world applications, including sound-source separation and noise suppression for compact devices.
Paper Presenters
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

2:30pm GMT

Enhancing Reliability in Heavy Duty Autonomous Mobile Machines through Fault Tolerant Edge Computing
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Authors - Kalle Hakonen, Jussi Aaltonen, Kari Koskinen
Abstract - This paper presents a novel fault-tolerant edge computing architecture for heavy-duty autonomous mobile machines in industrial environments. The proposed system integrates two virtual machine hosts with a circular topology of four Ethernet switches, ensuring network resilience and operational continuity. A key feature is the implementation of automatic takeover protocols, enabling seamless transition between hosts during hardware failures. The network infrastructure leverages Rapid Spanning Tree Protocol (RSTP) and additional loop protection mechanisms to maintain stability and achieve rapid fault recovery. Virtual machines running Ubuntu Linux or FreeBSD are strategically deployed to handle specific tasks with critical services replicated for enhanced reliability. The system incorporates advanced data management through a PostgreSQL database with master-slave replication. Comprehensive fault tolerance mechanisms, including redundant connections and graceful degradation capabilities, ensure robust performance in challenging industrial settings. This architecture significantly enhances the reliability and autonomy of heavy-duty mobile machines, addressing the critical need for uninterrupted operation in industrial automation applications.
Paper Presenters
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

2:30pm GMT

The Utilization of E-Government Services and the Paradox of Digital Support in the Pandemic Era
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Authors - WookJoon Sung
Abstract - The COVID-19 pandemic necessitated an accelerated shift toward digital service provision within the public sector, creating both opportunities and challenges with respect to digital inclusivity. This study critically examines the roles of digital literacy, intrinsic motivation, and digital support in the utilization of COVID-19-related e-government services, revealing a notable paradox: reliance on digital support is associated with diminished engagement in digital public services. Using data from the 2020 Digital Divide Survey by South Korea's National Information Society Agency (NIA), this research employs multinomial logistic regression to elucidate the dynamics at play. Findings suggest that policies advancing digital competence, fostering user autonomy, and addressing restricted access to support in crises are essential for equitable digital service access during future crises.
Paper Presenters
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

2:30pm GMT

Towards adaptive individual health parameter monitoring
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Authors - Rareș Arvinte, Diana Trandabat
Abstract - In modern hospital approach, patients diagnosis, analysis and administered medication is noted for a better view about his wellbeing but this information are not used to maximum capacity because of the missing analytic and adaptation using multiple parameters. This can improve medical process but also the monitoring of the patient and the possible future outcome of his different illnesses. In this paper, we approach the idea of extracting information from dataset and generating reports, which adapted for medical needs, can outperform automated approaches which not necessary find the real parameters that count.
Paper Presenters
Tuesday February 18, 2025 2:30pm - 2:45pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

2:45pm GMT

A Detailed Study on Model Reduction Strategy of Unstable Dynamical Systems
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Authors - Zarin Tasnim, Atia Afroz, Mohammad-Sahadet Hossain, Oshin Mumtaha, Tahiya Tasneem Oishee
Abstract - Model reduction aims to decrease this computational strain by producing significantly smaller models that are faster and less expensive to simulate than the original large-scale systems which play a vital role in the complex physical phenomena. The goal of this paper is to gather information from different existing research works and summarize the current advancements in managing index-2 discrete-time (DT) descriptor-systems. In this paper, we look at three different methods for dealing with DT systems and compare them based on how well they work and how accurate they are. We also study the iterative approaches for the approximate solutions of the Smith method and the Alternating Direction Implicit (ADI) methods are used in different ways to make the Balanced Truncation (BT) method more effective. The iterative rational Krylov algorithm (IRKA) is used to approximate the original model by moment-matching. A comparative analysis of the different Model Reduction Methods (MOR) algorithms is illustrated to recognize the pros and cons of different approaches and the appropriate use of different MOR strategies.
Paper Presenters
avatar for Zarin Tasnim

Zarin Tasnim

Bangladesh
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

2:45pm GMT

Enhancing Carbon Stock Estimation Using Machine Learning Models: A Comparative Analysis of MLP, RNN, and Autoencoder Approaches
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Authors - John Khoo, Rayner Alfred, Khalifa Chekima, Rayner Pailus, Chin Kim On, Ervin Gubin Moung, Raymond Alfred, Oliver Valentine Eboy, Normah Awang Besar Raffie, Ashraf Osman Ibrahim Elsayed, Nosius Luaran
Abstract - Climate change remains one of the most pressing global challenges, with effective management of carbon stocks in forests playing a vital role in mitigating its impact. Carbon stock estimation, which quantifies the amount of carbon stored in various biomass forms such as trees, soil, and deceased organic matter, is essential for understanding the role of forests in carbon sequestration and developing strategies to reduce carbon emissions. Traditional methods for carbon stock estimation are often labour-intensive, time-consuming, and lack the precision required for large-scale analysis. Advances in machine learning and remote sensing technologies offer a significant opportunity to improve the accuracy and efficiency of carbon stock estimation. This paper investigates the use of machine learning models, specifically Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Autoencoder, for estimating carbon stocks using datasets from sources such as Landsat 7, NDVI, and SAR. Comprehensive analyses were conducted with different train-test splits (70/30, 60/40, 50/50), sample sizes (1,000; 10,000; 100,000), learning rates (0.01, 0.05, 0.1), and epochs (1,000, 10,000, 20,000). The results indicate that the AE model consistently outperforms MLP and RNN models, demonstrating superior predictive accuracy (lower RMSE and MAE) and reliability (higher IOA). The AE model's robustness was evident across all settings, making it the most effective model for carbon stock estimation. In contrast, the RNN model showed higher error rates and longer training times, particularly with smaller sample sizes and higher learning rates. The MLP model exhibited moderate performance. These findings underscore the importance of model selection and hyperparameter tuning in enhancing the accuracy of carbon stock estimation. The study highlights the potential of the AE model as a valuable tool for environmental monitoring and management, providing insights into improving machine learning applications for sustainable ecosystem assessment.
Paper Presenters
avatar for Rayner Alfred
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

2:45pm GMT

Harnessing Business Intelligence for Sustainable Fraud Mitigation in Fintech Enterprises
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Authors - Noura Metawa, Atia Hussain, Saad Alsunbul
Abstract - The dynamic landscape of Financial Technology (Fintech) enterprises has catalyzed transformative innovations, yet concurrently spurred challenges in fraud detection and mitigation. This study investigates the integration of Business Intelligence (BI) strategies in addressing sustainable fraud mitigation within the Fintech domain. Grounded in the problem statement of class imbalance, our methodology leverages Adaptive Boosting (AdaBoost) and Synthetic Minority Over-sampling Technique (SMOTE) to augment fraud detection models. Through empirical analyses, including feature importance assessments and ROC curve evaluations, this research reveals the efficacy of BI-driven approaches in enhancing predictive accuracy, discerning critical fraud indicators, and mitigating the challenges posed by class imbalance. Visual representations, such as correlation maps, elucidate intricate relationships among variables, offering crucial insights for model refinement. The study concludes by emphasizing the pivotal role of BI-integrated strategies in fostering resilient fraud mitigation frameworks within Fintech enterprises, paving the way for continued innovation and trust-building in the evolving landscape of financial technologies.
Paper Presenters
avatar for Noura Metawa

Noura Metawa

United Arab Emirates
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

2:45pm GMT

Intelligent Approaches to Automate Quality Control in Manufacturing
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Authors - Malinka Ivanova, Petya Petkova
Abstract - A manufacturing process, including in the field of electronics, must be organized in such a way as to ensure the production of quality items. This can be achieved by using and following certain quality control procedures and methods. Quality control is usually carried out at several stages of manufacturing to check that there are no deviations in product parameters, thus to ensure compliance with the production specification. Recently, in the scope of concepts related to industry 4.0, techniques from machine learning and artificial intelligence have also been applied, making quality control systems increasingly intelligent, thereby greatly assisting quality experts. The aim of the paper is to map the current scientific achievements regarding the application of machine learning in the field of quality control through bibliometric analysis and to present the results of performed experiments with data from a real manufacturing process. At the created predictive models that solve a classification task with two classes pass or fail the quality check, the following learning methods are applied: a) artificial neural network with optimization of parameters, b) principal component analysis and deep learning, c) Random Forest. Performance of the three learning methods is high as the second method is particularly suitable.
Paper Presenters
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

2:45pm GMT

LODEC: LODCO Edge Computing for TDOA Localization
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Authors - Isaac Osei Nyantakyi, Samuel Akwasi Danso, Justice Odoom, Patrick Bobbie, Dennis Gookyi, Emmanuel Osei-Mensah
Abstract - Indoor localization is a cornerstone technology for smart environments, including smart homes, factories, and healthcare systems. Time Difference of Arrival (TDoA) techniques are widely recognized for their high localization accuracy but face significant challenges in managing computational demands and latency, particularly in Internet of Things (IoT) scenarios. This paper introduces the Lyapunov Optimization based Dynamic Computation Offloading (LODCO) algorithm, a novel framework designed to enhance TDoA localization by leveraging the capabilities of Mobile Edge Computing (MEC). By dynamically balancing computation between edge servers and local devices, LODCO minimizes latency, optimizes energy consumption, and adapts to diverse environmental and user requirements. The proposed algorithm addresses the inherent trade-offs in TDoA localization systems, achieving reduced execution delays and maintaining precise positioning even in ultra-dense network (UDN) environments characteristic of 5G deployments. Extensive simulations demonstrate that I. O. Nyantakyi et al. LODCO consistently outperforms state-of-the-art algorithms, such as WAKNN-PSO and MLE-PSO, in terms of positioning accuracy, energy efficiency, and bandwidth utilization. These findings highlight the scalability and practicality of LODCO in enabling real-time indoor localization within IoT-driven ecosystems.
Paper Presenters
Tuesday February 18, 2025 2:45pm - 3:00pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

3:00pm GMT

EDV-ML: Enhanced Distance Vector Hop Localization for Wireless Sensor Network using machine learning
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Authors - Samuel Akwasi Danso, Isaac Osei Nyantakyi, Justice Odoom, Patrick Bobbie, Dennis Gookyi, Emmanuel Osei-Mensah
Abstract - This paper introduces an advanced localization algorithm for wireless sensor networks (WSNs) called Enhanced Distance Vector Hop with Machine Learning (EDV-ML), addressing key limitations of the traditional Distance Vector Hop (DV-Hop) algorithm. The proposed EDV-ML algorithm employs a supervised learning model to correct average hop distances based on network parameters, significantly improving accuracy. Additionally, it integrates reinforcement learning techniques to optimize node coordinates, ensuring reduced localization error. Comprehensive MATLAB simulations highlight the superior performance of EDV-ML compared to traditional DV-Hop and its variants, demonstrating substantial enhancements in positioning accuracy. The proposed approach achieves these improvements without requiring additional hardware or extensive modifications, offering a practical and efficient solution for real-world WSN deployments. This work makes a meaningful contribution to localization algorithms by combining machine learning and optimization techniques to enhance accuracy and robustness.
Paper Presenters
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

3:00pm GMT

Exploring the Evolution of Identity Theft: Emerging Trends, Tactics, and Mitigation Strategies in the Digital Age
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Authors - Milan Adhikari, Deblin Gautam, Prabhjot Kaur, Dharmrajsinh Vaghela, Saman Shojae Chaeikar, Maryam Khanian Najafabadi
Abstract - This study aims to explore the development of identity theft in the modern world with special attention to contemporary trends, cybercriminals’ techniques, and measures of protection in the context of the digital environment. The paper aims to identify a new generation of identity theft that has evolved from the physical world to the digital environment through phishing, social engineering, account takeovers, synthetic identity theft, and data breaches. It further discusses how new technologies such as Blockchain, Biometrics, and Artificial intelligence affect identity theft prevention and detection. Furthermore, it assesses different measures to reduce the threat, such as awareness raising, legislation, cybersecurity measures, and technologies. In light of this, this research seeks to contribute to the literature by presenting an account of identity theft in the contemporary world and presenting appropriate solutions to mitigate this menace and protect personal information in the current digital age.
Paper Presenters
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

3:00pm GMT

Generation of Clothing Patterns Based on Impressions Using Stable Diffusion
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Authors - J N Htoi Sann Ja, Kaede Shiohara, Toshihiko Yamasaki, Miyuki Toga, Kensuke Tobitani, Noriko Nagata
Abstract - Personalized products based on individual preferences have been considered to improve personal well-being and consumer satisfaction. This approach helps reduce waste and conserve resources. With artificial intelligence enabling personalization, consumers can easily access products that match their preferences without the need for specialized knowledge or professional expertise. Advances in artificial intelligence, text-to-image models in particular, have enabled the generation of impressive images from textual descriptions. However, existing models lack the ability to generate images based on visual impressions. In this paper, we propose a text-to-image diffusion model that incorporates visual impressions into the image generation process. Our model extends the stable diffusion architecture by introducing a multi-modal input system that processes text descriptions, pattern images, and quantified visual impressions. Experimental validation confirmed the positive correlation between generated and original images across multiple impression metrics, demonstrating the model’s effectiveness in preserving impression-based characteristics. These results suggest that our approach successfully bridges the gap between textual descriptions and visual impressions in image generation.
Paper Presenters
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

3:00pm GMT

Radio Environment Maps as a Tool for Occupancy Detection in Indoor Environment
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Authors - Toufiq Aziz, Shafi Ullah Khan, Insoo Koo
Abstract - Smart building functionalities are crucial for optimizing energy consumption through accurate occupancy detection in indoor environments. This study explores a novel approach to occupancy detection by leveraging Radio Environment Maps (REMs). Unlike conventional methods, the proposed technique involves creating detailed REMs across diverse indoor environments, considering variables such as room size, furniture arrangements, and the presence of the occupants. A predictive model is then applied to the REM data to detect occupancy and validate the accuracy of the predictions. The method was tested across multiple scenarios, including rooms with and without occupants, demonstrating its adaptability to different environmental conditions. By analyzing signal behavior variations, this method provides insights into how environmental factors impact detection performance. The results demonstrate the robustness of the proposed REM-based approach, achieving high prediction accuracy and confirming the effectiveness of the REMs in capturing the environmental nuances that influence signal behavior. This approach offers a flexible and scalable alternative for occupancy detection, with potential applications in energy-efficient building management and smart environment design.
Paper Presenters
avatar for Toufiq Aziz

Toufiq Aziz

The Republic of Korea
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

3:00pm GMT

The Impact of Communication and Leadership on the Implementation of Construction Projects
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Authors - Ramabele Matlala, Benita G. Zulch, Partson Paradza
Abstract - Communication and leadership are key to the success of the construction of projects. The study's main goal was to evaluate the impact of communication and leadership in the construction sector and its influence on the successful implementation of projects. The researchers adopted a quantitative research approach and data was collected using a standardised questionnaire. A total of 53 survey questionnaires were distributed to professionals employed in the construction sector in North-West Province and 40 were returned. The respondents comprised Engineers, Architects, Quantity Surveyors, Project Managers, and Contractors. It has been discovered that communication and leadership are associated since communication is a fundamental component of leadership. The paper recommends that the transactional leadership approach be employed in construction projects.
Paper Presenters
avatar for Benita G. Zulch

Benita G. Zulch

South Africa
Tuesday February 18, 2025 3:00pm - 3:15pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

3:15pm GMT

From Disadvantaged Beginnings to IT Careers: Exploring Career Decision-Making, Parental Influence, and Technological Aspirations Among South African Students
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Authors - Mahle Shabalala, Nita Mennega
Abstract - This study explores the influence of parental perspectives on career decision-making among South African students, specifically focusing on Information Technology (IT) careers. In an age of rapid technological advancement, many students are steered toward career paths based on family beliefs, cultural values, and socio-economic factors, sometimes overlooking the growing opportunities within the IT sector. This research is significant because it highlights the role of IT careers in shaping personal and economic success in a digitally-driven world. Using qualitative methods, including interviews with 17 IT professionals, the study examines the factors that influence parents’ career advice to their children, such as job security, financial freedom, and the integration of AI and machine learning into various fields. The findings show that IT workers encourage their children to pursue careers with long-term job security and technological relevance, particularly in AI and machine learning. This suggests that parents’ career recommendations are deeply intertwined with the evolving demands of the workforce, with a focus on technology-driven fields. The implications of this study point to the need for greater awareness and understanding of IT career opportunities, emphasising the importance of informed decision-making for students and their families to align with future workforce needs.
Paper Presenters
avatar for Mahle Shabalala

Mahle Shabalala

South Africa
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

3:15pm GMT

Internet of Things and Artificial Intelligence Powered Crop Suitability Detection System for Sustainable Farming
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Authors - A S M Ahsanul Sarkar Akib, Abu Zahid Md Jalal Uddin, Fatema Jahan Sifa, Mahadir Islam, Md. Easin Arafat, Touhid Bhuiyan
Abstract - This paper presents an innovative IoT-based autonomous farming system utilizing machine learning models to assist farmers in determining suitable crops based on real-time environmental data. The system integrates IoT sensors, including soil pH, NPK, temperature, and humidity sensors, to collect data from the field. The ESP-8266 NodeMCU processes this data and transmits it to a cloud database. A range of machine learning algorithms were applied to the dataset, including Logistic Regression, Gaussian Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree Classifier, Extra Trees Classifier, Random Forest, Bagging Classifier, Gradient Boosting, and AdaBoost. The highest accuracy was achieved with the Random Forest Classifier (97.05%), followed closely by the Bagging Classifier (96.59%) and Gradient Boosting (96.36%). The AdaBoost model showed poor performance with an accuracy of 10.23%. The system’s predictions are accessible to farmers via a web or mobile application, enabling them to make informed decisions about crop cultivation. This IoT and machine learning-based approach reduces human intervention, optimizes farming practices, and enhances crop yield potential. The system provides real-time crop recommendations, making farming more efficient and sustainable. Use of appropriate algorithms on the sensed data can help in recommendation of suitable crop.
Paper Presenters
avatar for Mahadir Islam

Mahadir Islam

Bangladesh
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

3:15pm GMT

Optimizing Path Planning and Manipulation Strategies for Automated Lettuce Harvesting in Precision Agriculture
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Authors - Eduardo Velez, Alejandra Cruz, Felipe Coyotl, Humberto Arroyo, Alain Corona, Michelle Carreon
Abstract - The integration of robotics into agriculture has significantly advanced precision farming practices by reducing labor costs associated with repetitive tasks. This paper presents a comprehensive approach to optimize path planning and manipulation for agricultural robots involved in lettuce harvesting. We explore modifications to the A* algorithm for efficient navigation in unstructured agricultural environments, implement trajectory smoothing techniques for enhanced control, and develop inverse kinematics solutions for precise manipulation of the robotic arm. The proposed methods address the challenges of navigating between crop rows, avoiding obstacles, and ensuring accurate positioning of the end-effector for a successful harvest.
Paper Presenters
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

3:15pm GMT

Simulation of Distributed Sensing Applications: Tradeoff between Accuracy and Simulation Time
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Authors - Sebastian Kiunke, Lars Wischhof
Abstract - Direct communication between mobile devices enables a wide range of distributed sensing applications, e.g. environmental monitoring or the generation of decentralized pedestrian density maps. In many cases, data generated by these distributed sensing applications is evaluated by artificial intelligence (AI), for example, to predict critical situations in the future and react before these occur. However, training AI models that can be used for this task requires a large amount of data. Detailed discrete event simulations of relevant situations, such as crowded areas, are time-consuming, particularly in the case of many communicating sensors. In this paper, the application layer within the network simulation is subset by two less complex communication models to reduce the simulation time. To evaluate if the simplified modelling leads to significantly different simulation results, a typical example application is evaluated. It creates a local pedestrian density map based on direct communication. Initial results confirm that the detailed 4G/5G communication model can be replaced by a simpler model to reduce the required simulation time by up to 56% in specific scenarios. However, one of the two evaluated example scenarios also leads to a significant underestimation of the mean square error of the sensed data values.
Paper Presenters
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

3:15pm GMT

Statistical Feature Extraction for Stock Price Prediction Machine Learning-based Model for Indonesia's Five Leading LQ45 Companies
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Authors - Lili Ayu Wulandhari , Aditya Kurniawan, Nathania Christy Nugraha, Natasha Hartanti Winata
Abstract - The stock market, as a fundamental component of a nation's economic structure, profoundly impacts the course of economic progress. The stock market's performance can influence foreign investment and the growth of the real sector. Stock fluctuations are essential for providing valuable insights to market participants and investors, allowing them to develop strategies that foster growth and macroeconomic stability. Nonetheless, precisely forecasting stock values is challenging. This work investigates statistical feature extraction for stock price prediction utilizing machine learning approaches on Indonesia's leading five LQ45 companies: BBCA, ASII, BBRI, BMRI, and TLKM. Daily stock price data from 2019 to 2024 is employed, emphasizing statistical characteristics such as mean, standard deviation, skewness, kurtosis, and interquartile range. The features are examined utilizing decision trees, random forests, and multilayer perceptrons (MLPs) with hyperparameter optimization. The MLP model demonstrates superior accuracy, achieving an average 𝑅2 𝑆𝑐𝑜𝑟𝑒 of 0.95 and a MAE that is 92.52% lower than the standard deviation of the actual stock price. The results indicate the effectiveness of statistical feature-based machine learning for accurate stock price prediction, implying possible uses in financial decision-making and market analysis.
Paper Presenters
Tuesday February 18, 2025 3:15pm - 3:30pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

3:30pm GMT

Convolutional Neural Network Based Animal Classification: A Comparative Study of Custom Architecture VGG16 and ALexNet
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Authors - Kanda Tshinu Patrick, Chunling Tu, Owolawi Pius Adewale, Antonie Smith
Abstract - This paper presents a comparative study for animal classification using Convolutional Neural Networks (CNNs), specifically based on the VGG16, AlexNet, and a custom CNN architecture. Traditional methods for classifying animals rely heavily on human visual ability, which is time-consuming, labor-intensive, and prone to inconsistencies that can compromise the accuracy of species identification. In contrast, CNN-based techniques leverages automated features extraction. Primarily focusing on body and facial features, to deliver more consistent and reliable classification outcomes. The fields of computer vision and image processing have gained significant attention due to their effectiveness in addressing classification challenges across domains such as agriculture, wildlife conservation, and biodiversity research. The development of CNNs has become a critical tool in these areas, enabling automated systems to monitor livestock, track animal behaviour, and detect wildlife, enhancing both animal management and conservation efforts. In this study, three CNN architectures—VGG16, AlexNet, and a custom CNN—were trained on a dataset comprising 5,400 images of animals across 90 classes. The results show that VGG16 achieved the highest classification accuracy of 91.51%, followed by the custom CNN with 84.57%, and AlexNet with 79.02%. These findings demonstrate the potential of deep learning for accurate species identification. Furthermore, the use of pre-trained models like VGG16 can enhance classification performance, while custom models can provide competitive results with fewer computational resources. This study highlights the effectiveness of CNNs in animal classification and underscores their potential for discovering new observations, including the identification of previously undocumented species within the same class.
Paper Presenters
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

3:30pm GMT

Drone-based Tomato Fruit Detection through Hardware-Accelerated YOLO Deployment
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Authors - Abdellah Islam Kafi, Antonio P. Sanfilippo, Raka Jovanovic, Sa’d Abdel-Halim Shannak
Abstract - In this paper, we develop a drone-based solution for detecting productivity characteristics of tomato crops inside agricultural greenhouses using the YOLO8 computer vision model; a mobile phone is used to deploy the trained model. The implementation leverages the Apple Neural Engine (NE), a hardware accelerator module embedded in recent Apple mobile phones, to enable fast and efficient inference. Our video acquisition component also employs a DJI remote controller that streams live video from the drone to the mobile app for processing. The main objective is to perform rapid and precise detection of tomatoes within greenhouses, where drones can improve efficiency and coverage. We describe the model architecture and various optimization techniques suitable for embedded-platform deployment. The experimental study demonstrates the system’s effectiveness in detection accuracy and inference time when utilizing NE compared to CPU-based inference. We also compare accuracy, model size, and inference speed across variants of the YOLO algorithm.
Paper Presenters
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Aldgate Suite - 2E America Square Conference Centre, London, United Kingdom

3:30pm GMT

IoT-Enabled Smart System for Real-Time Alerts and Management of Sandstorms and Landslides
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Authors - Thirupathi Regula, Warda Al Hoqani, Sunitha Cheriyan, Sharmila K. Reddy, Naveen Kumar, Shazia Tazeen, Kiran Kumar S. G
Abstract - Natural disasters, driven by changes in climate and topography, pose significant risks to human life and economic stability. Events such as earthquakes, landslides, tsunamis, and cyclones result in substantial ecological damage and loss of life. With climate change accelerating, these disasters are becoming increasingly unpredictable, particularly in vulnerable regions like forests and deserts. This study introduces an IoT-enabled smart system designed to provide real-time alerts and manage sandstorms and landslides more effectively. The system integrates IoT technologies with automated alert mechanisms to deliver timely warnings via mobile devices. It employs various sensors, including accelerometers and inclinometers to monitor ground movement for landslide detection, and particulate matter (PM) sensors, anemometers, and humidity sensors to detect sandstorm conditions. Utilizing Geographic Information System (GIS) and Global Positioning System (GPS) technologies, the system provides real-time updates and precise location data. Enhanced communication and situational awareness enable efficient emergency response and resource deployment. This innovative approach aims to improve survival rates, facilitate faster rescue operations, and strengthen disaster preparedness, particularly in remote, disaster-prone areas.
Paper Presenters
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom

3:30pm GMT

The Sustainable Business Models Architectural Design in the Frame of Industry 4.0
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Authors - Pavel Malyzhenkov, Maurizio Masi, Fabrizio Rossi
Abstract - the development of the Internet and other Industry 4.0 enabling technologies has primarily led to the acceleration of all processes and an increase in the volume of required information. As a result it gives a significant boost to the development of both businesses and society as a whole, while the assessment of its economic and social impact varies considerably. This is why the development of Industry 4.0 technologies is directly linked to the development of sustainable development strategies which must be incorporated in the enterprise architecture design.
Paper Presenters
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

3:30pm GMT

Unraveling Jingle-Jangle Fallacies in Digital Assistant Technologies: A Comprehensive Systematic Review and Research Agenda
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Authors - Niklas Preiss, Markus Westner
Abstract - Digital assistant technologies (DATs) such as chatbots, virtual assistants, and intelligent agents have gained widespread attention, yet inconsistent terminology remains a critical challenge. The fragmented nature of previous research has led to significant confusion due to overlapping and interchangeable use of terms across industries. This systematic literature review, following the PRISMA protocol, consolidates the current state of knowledge on DATs and addresses the prevalent jingle-jangle fallacies in their terminology. Analysis of 137 articles identified key characteristics, applications, and conceptual overlaps of various DATs, uncovering 39 distinct technologies categorized under three overarching concepts: assistants, chatbots, and agents. Despite shared functionalities, terminological inconsistencies persist across different sectors, presenting challenges for both academic research and practical implementation. This review emphasizes the need for standardized terminology and clearer classification frameworks to facilitate broader DAT adoption across organizational contexts.
Paper Presenters
Tuesday February 18, 2025 3:30pm - 3:45pm GMT
Fleet Suite - 2D America Square Conference Centre, London, United Kingdom

3:45pm GMT

Acceleration Methods for Finding Measurement Points for Testing Power TSVs in Stacked 3D-IC
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Authors - Koutaro Hachiya
Abstract - As a method for testing power TSVs (Through Silicon Vias) in 3D-ICs, prior studies have proposed detecting open defects by measuring the re-sistance between power pads placed directly beneath the TSVs. However, opti-mizing the resistance measurement points and deriving the defect coverage for this test require repeated circuit simulations to calculate the resistance between power pads, which is time-consuming. To accelerate this process, this paper proposes a method to estimate defect coverage using sampling and optimize measurement points through Bayesian optimization. Numerical experiments demonstrated that the time required for defect coverage estimation was acceler-ated by a factor of 10 compared to methods without sampling. Additionally, the time needed to optimize measurement points was reduced by approximately 1.6 times compared to the conventional Exhaustive Neighborhood Search method when the approximate locations of the optimal measurement terminals were unknown.
Paper Presenters
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Newgate Suite - 2B America Square Conference Centre, London, United Kingdom

3:45pm GMT

Selection and prioritizing Quality of Services Based on Qualitative and Quantitative Approach
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Authors - Mamoun Ghaleb Awad, Ibrahim Mohammed Dweib
Abstract - As governments worldwide increasingly transition towards digital platforms to enhance service delivery and citizen engagement, the selection of appropriate e-government services becomes a critical aspect of effective governance. This research provides an in-depth analysis and framework for service selection in e-government, addressing the challenges and complexities inherent in this crucial decision-making process. By integrating theoretical perspectives with empirical evidence, the study offers a comprehensive understanding of the factors affecting service selection, thereby offering valuable insights for policymakers, practitioners, and researchers in the field of e-governance. The research aims to develop a technological framework that emphasizes the curation and improvement of high-quality services within the e-government context, ultimately enhancing service delivery. A mixed-methods approach is employed, combining qualitative (survey) and quantitative (mathematical metrics) methodologies to evaluate the quality of e-government services. This methodology systematically organizes and classifies various services according to their significance and demand, culminating in effective service identification and selection. The paper details the essential activities of domain identification and service selection, providing clarity on the complex process of evaluating service quality within the e-government framework.
Paper Presenters
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Bishopsgate Suite - 2A America Square Conference Centre, London, United Kingdom

3:45pm GMT

Sensor Data Synchronization based on Estimation of Commonly Detected Events in Wireless Sensor Networks
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Authors - Katsuhiro Nagano, Hiroaki Higaki
Abstract - In a wireless sensor network, sensor nodes transmit sensor data with information about detected events including local clock values at times when occurrences of the events are detected to a fusion center. The sensor data is stored in a database and analyzed. Since local clocks in wireless sensor nodes are usually not synchronized precisely due to unexpectable transmission delay of control messages for clock synchronization which requires high communication overhead, this paper proposes sensor data synchronization which adjust the local clock values associated to stored sensor data. For local clock value adjustment, this paper proposes a novel method based on estimation of relative skew and offset between local clocks in neighbor wireless sensor node whose observable areas are overlapped. Here, estimation of commonly detected events by the wireless sensor nodes is a key technique. By our proposed method, with no additional communication overhead, local clock values are consistently adjusted to analyze their temporal relations such as precedence ones.
Paper Presenters
Tuesday February 18, 2025 3:45pm - 4:00pm GMT
Walbrook Suite - 2C America Square Conference Centre, London, United Kingdom
 
Wednesday, February 19
 

9:28am GMT

Opening Remarks
Wednesday February 19, 2025 9:28am - 9:30am GMT
Wednesday February 19, 2025 9:28am - 9:30am GMT
Virtual Room A London, United Kingdom

9:28am GMT

Opening Remarks
Wednesday February 19, 2025 9:28am - 9:30am GMT
Wednesday February 19, 2025 9:28am - 9:30am GMT
Virtual Room B London, United Kingdom

9:28am GMT

Opening Remarks
Wednesday February 19, 2025 9:28am - 9:30am GMT
Wednesday February 19, 2025 9:28am - 9:30am GMT
Virtual Room C London, United Kingdom

9:28am GMT

Opening Remarks
Wednesday February 19, 2025 9:28am - 9:30am GMT
Wednesday February 19, 2025 9:28am - 9:30am GMT
Virtual Room D London, United Kingdom

9:30am GMT

A System for Performance Measurement in Higher Education
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Reena (Mahapatra) Lenka, Rajiv Divekar, Jaya Chitranshi
Abstract - The performance measurement analysis system is suggested to overcome the problems various higher education sectors face in improving their performance. This performance measurement system satisfies all the requirements of scholars related to institute requirements. This system also satisfies the faculty’s needs as well as they can keep track of the student details, attendance, and marks, upload assignments, and have a fair idea regarding the students. Also, they can track how they can improve their performance. This system also satisfies the administration's need to keep students' records per the institutes' requirements. When followed and implemented in the higher education sector, this system would help improve the institute's performance to a greater extent, increasing its brand name.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Creating an Academic Performance Management Model for Higher Education
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Jaya Chitranshi, Rajiv Divekar, Reena (Mahapatra) Lenka
Abstract - ‘Performance management’ should be the focus of any institution of ‘higher education’ to achieve its sublime objective of educating, training and steering mature minds. The purpose of transforming students into high-performance individuals can be achieved completely, when there are certain checkpoints and steps to the process of ‘‘performance management’’. The paper attempts to identify existing gaps in literature with respect to ‘performance management’ in ‘higher education’. This research paper proposes a nine-step model of ‘performance management’ for increasing performance of students in ‘higher education’. These steps are (i.) Goal-setting, where the targets should be set for students for one term; (ii.) Coaching and guiding, that should be done to make students achieve the goals set; (iii) Performance measurement, that should be done to assess performance with respect to goals set; (iv) Mentoring, that should be done to help students explore their strengths/potential/chances/ opportunities of improving performance with respect to set goals; (v) Counselling, that should be done to help students identify areas where they still lag or where their potential is still not used; to improve performance with respect to specific goals set; (vi) Performance measurement, that should be done to assess performance again; (vii) Performance Aggregate for the goal(s), that should be measured with respect to a specific goal, and for all goals then combined; (viii) Reward/ Advisory, that should be decided based on the aggregate of performance; (ix) New Goal(s)/ Revising and recalibrating goals-Based on the reward/ advisory and the aggregate of performance, new goals can be set/goals can be revised or recalibrated for the next term. It is extremely essential that ‘continuous feedback’ be provided to students in ‘higher education’ institutions, so that students get a clear direction towards improving their performance.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Designing a System of Performance Feedback Communication in Higher Education
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Jaya Chitranshi, Rajiv Divekar, Reena (Mahapatra) Lenka
Abstract - One of the main objectives of higher education is to assist students improve their academic performance. This objective can only be accomplished when students consistently receive tailored feedback on how to improve their performance levels. This research-paper focusses on the process and model of performance feedback communication in higher education. The process-flow of performance feedback communication illustrates the input received through student, faculty, feedback type, login and model user. On the basis of the inputs, feedback-reports (student report, faculty report, feedback-type report and model user report) can be created and user login details can be checked. The step-wise model of performance feedback communication in higher education provides continuous performance feedback to students in higher education through 4 important steps. Step 1) Monthly Feedback collection-360 Degree, Step 2) Matching with Expectations, Step 3) Continuous Feedback Communication, Step 3. (A) Positive Feedback, Step 3. (B) Constructive Feedback, Step 3. (C) Supplement: Active Listening, Step 4.A.(i) Positive Feedback-script, Step 4.A.(ii) Positive Feedback-Mode of Communication-In public, Step 4.B.(i) Constructive Feedback-script, Step 4.B.(ii) Constructive Feedback-Mode of Communication-In private.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Evaluation of Prediction Model for Mobile Educational Games
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Eng Bah Tee, Insu Song
Abstract - Mobile educational games have arisen as a fascinating tool to teach difficult concepts in an interactive and engaging manner. Mobile educational game uses a game type like puzzle, strategy, role-playing and so forth to drive the education of learning content. Currently game type is something selected by the game designer or programmer. Previous research study has mentioned that game type and lesson content is a critical area that requires more research. At the moment, a teacher or game designer is not too sure what game type would be best to teach a lesson on Geography or Mathematics. In fact, it has been found in the previous study that game type does have a significant impact on learning outcome and experience. To capitalize on the research gap for game type, we have therefore embarked on Stage 2 of our study to use artificial intelligence (AI). A machine learning model is employed to predict the evaluation score of the game type of mobile educational game employed to teach a subject lesson and to recommend the best game type for teaching the lesson. We then proceeded to Stage 3 and evaluated the performance of the AI model by creating a test set of twenty games and twenty undergraduates were recruited at an Indonesian university to evaluate the games. The average score of all mobile games evaluations is above the average of 3.5, thus proving the hypothesis H1 set out for Stage 3.
Paper Presenters
avatar for Eng Bah Tee

Eng Bah Tee

Singapore
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

On the Fast Track to Full Gold Open Access
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Robert Kudelic
Abstract - The world of scientific publishing is changing; the days of an old type of subscription-based earnings for publishers seem over, and we are entering a new era. It seems as if an ever-increasing number of journals from disparate publishers are going Gold, Open Access that is, yet have we rigorously ascertained the issue in its entirety, or are we touting the strengths and forgetting about constructive criticism and careful weighing of evidence? We will therefore present the current state of the art, in a compact review/bibliometrics style, of this more relevant than ever hot topic, including challenges and potential solutions that are most likely to be acceptable to all parties. Suggested solutions, as per the performed analysis, at least for the time being, represent an inclusive publishing environment where multiple publishing models are competing for a piece of the pie and thus inhibiting each other’s flaws. The performed analysis also shows that there seems to be a link between trends in scientific publishing and tumultuous world events, which in turn has a special significance for the publishing environment in the current world stage—implying that academy publishing has potentially now found itself at a tipping point of change.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Student Performance Management in Higher Education: A Bibliometric Analysis
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Jaya Chitranshi, Rajiv Divekar, Reena (Mahapatra) Lenka
Abstract - To satisfy its high purpose of shaping, developing, and directing adult minds, an institution of higher learning should have ‘performance management’ as its top priority. When there are clear phases and benchmarks in the ‘performance management’ process, the aim of transforming students into high-achieving individuals can be adequately achieved. The paper's objective is to identify any research gaps in ‘performance management’ in ‘higher education’ and make recommendations for future courses using Bibliometric Analysis.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Amalgamating DP-LCS-Assisted Abridgement Assessment with BERT-Based Sentiment Analysis for Early Warning and Prediction of Academic Performance
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Koichi Akashi, Hibiki Ito, Atsuko Yamashita, Katsuhiko Murakami, Sayaka Matsumoto, Kunihiko Takamatsu, Tetsuhiro Gozu
Abstract - In recent times, a ground-breaking approach termed abridgement has been advocated for assessing reading comprehension of students in a distinct manner. The operation requires them to shorten the size of the given passage by only deleting the words or characters in it, while not permitting adding, paraphrasing, or swapping any of them. Thanks to its simple operational property, an efficient computational marking scheme has been invented employing the technique of dynamic programming (DP) by the following studies. These endeavors enabled further analyses to proceed, allowing for an ensuing research of reflections about teachings of abridgement collected from students using co-occurrence networks, to give an instance. Furthermore, evolution of this educational approach has led the researchers to suggest its unprecedented application to detect the presence of students who have potential difficulty with learning or specific tasks at an early stage, which is expected to contribute to the reduction of dropout rate in the long run. To make this happen, this research adopted the idea of utilizing large language models (LLMs), specifically Bidirectional Encoder Representations from Transformers (BERT) developed by researchers at Google in 2018, to automatically quantify the confidence levels of their understanding based on the technique of sentiment analysis of collected reflections. Combining grades calculated from DP-based algorithm with LLM newly invited to the research of abridgement, certain criteria have been established to issue warnings to identify those who are experiencing challenges early on.
Paper Presenters
avatar for Koichi Akashi

Koichi Akashi

United Kingdom
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

An Effective Resource Discovery Strategy for Fog Computing Driven by Computational Capabilities and Behavioral Characteristics
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Joao Bachiega Jr, Breno Costa, Leonardo R. Carvalho, Aleteia Araujo, Rajkumar Buyya
Abstract - The fog computing paradigm allows for the distribution of computing resources and services at the edge of the network, close to end users, complementing cloud computing. Due to the dynamicity of fog computing environments, resource discovery is a key process that aims to find new computational resources that are available to integrate into it. These resources compose fog nodes, devices considering computational capabilities (such as CPU, memory, and disk) and behavioral characteristics (such as availability, scalability, and mobility). Performing an optimized resource discovery with all those attributes is still a challenge. This article proposes an efficient approach to resource discovery in fog computing that considers the computational capability and behavioral characteristics to select fog nodes. The results show that it is at least 33% more efficient than a similar solution found in the literature.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Framework for Compiling Summary Reports in Business Intelligence Modules in Higher Education Institutions
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Kostadin Nevrokopliev, Silvia Gaftandzieva, Svetoslav Enkov
Abstract - Every organization needs business intelligence reports and analytics to help it make informed decisions. This creates the need to develop a framework for composing business intelligence reports and analytics. This article describes the structure of a software framework that allows reports to be grouped by given fields and the data to be aggregated. The reports are composed in a way that allows a broader view of the data, which helps managers in their decision-making process. Its flexible and versatile structure allows reports to be customized to fit the specific needs of a given organization. The paper discusses sample reports developed using the framework and shows how they help higher education institutions to manage their activities effectively.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Multi-Modal Retrieval-Augmented Generation for Enhanced E-Commerce Search
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Quang-Vinh Dang
Abstract - Product retrieval in e-commerce systems has traditionally relied on text-based matching between user queries and product descriptions. While recent advances have introduced image-based search capabilities leveraging deep learning techniques, existing systems typically operate in isolation, processing either textual or visual queries independently. However, contemporary user behavior increasingly demonstrates the need for multi-modal search capabilities, particularly as smartphones enable users to seamlessly combine photographic content with textual descriptions in their product searches. This paper presents a novel multimodal retrieval augmented generation (RAG) framework that unifies text and image inputs for enhanced product discovery. Our approach addresses the limitations of conventional single-modality systems by simultaneously processing and correlating both visual and textual features. By leveraging the complementary nature of these modalities, our system achieves more nuanced and contextually aware product matching. Experimental results demonstrate that our multi-modal RAG framework significantly improves search accuracy and relevance compared to traditional single-modality approaches. Furthermore, user studies indicate enhanced satisfaction and reduced search friction, suggesting meaningful improvements to the e-commerce user experience. Our findings contribute to the growing body of research on multi-modal information retrieval and offer practical insights for implementing more sophisticated product search systems in commercial applications.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Periodical Literature and Online Whiteboard as Alternatives to Textbook
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Nur Anneliza Abd Latip, Hanita Hanim Ismail, Harwati Hashim, Wardatul Akmam Din
Abstract - Textbook role in English language education is imperative in providing curriculum structure, standardising instruction, and maintaining the quality of teaching. Often it is an efficient material for teachers as it is readily available and reliable. Nonetheless, the one-size-fits-all material may not reflect real life language outcome. In reading section of textbook, the passages are simplified, adapted and inauthentic. In a class geared towards high stake reading examination, textbook is needed as it is mirroring language test format, making it a mock test situation. This research exposed students in tackling reading class to texts from periodical literature and explored them using an online whiteboard. The research is not striving to replace textbook, rather put forwards materials that provide learners with opportunity to have an active role in directing their own cognitive resources in reading. The study aims to investigate whether the intervention of using these materials have an effect towards reading achievement. A quasi-experimental design was applied to see quantitative evidence of reading marks. The participants were pre-university students studying at Universiti Malaysia Sabah, one of the public institutions in Malaysia. The findings show that students who are using the periodical literature and online whiteboard revealed significant changes and higher marks of reading achievement compared to the group of students who are only using textbook. The study is hoped to supplement reading class with relevant materials of authentic text and current technology.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Web-AR Based Support System for Food Tourism
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Makoto Hirano, Kayoko Yamamoto
Abstract - The purposes of tourism have been becoming diversified in recent years, and a form of travel known as food tourism is becoming increasingly popular. However, little research has been conducted on systems that support food tourism. Against such a backdrop, the present study aims to design, develop, operate and evaluate a food tourism support system that is supported to decide on restaurants for lunch and dinner, tourism spots to visit along the way, and routes to visit these destinations. The system comprises an original tourism plan creation system, web geographic information systems (Web-GIS) and web-augmented realty (Web-AR). In the present study, a location-based Web-AR system is developed. The system was operated for 30 days from December 22, 2023 to January 20, 2024, in Central Yokohama City of Kanagawa Prefecture, Japan. Total number of users was 50 and 20 tourism plans were created during the operation period. Based on the evaluation results, it is clear that the principal functions and the overall system were highly evaluated, regardless of food tourism experience or advance creation of tourism plan. Furthermore, it is evident that there was a high number of visits to the pages for most of the principal functions, and the system was used in a manner consistent with the purpose of the present study.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

A Device for AI and Extended Reality for Futuristic Organization
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Reena (Mahapatra) Lenka, Smita Mehendale
Abstract - This research paper examines this combination to understand better how Artificial Intelligence (AI) and Extended Reality (XR) might work together to change human experiences and capacities. Enhancing immersive environments primarily depends on artificial intelligence (AI), miming human cognitive capabilities. The study conducts a thorough literature review to comprehend the AI-XR synergy's goals, uses, constraints, and viewpoints. It emphasises how AI may complement human labour and how XR can produce multi-dimensional experiences, using examples from the aerospace, construction, and healthcare sectors. The paper describes the influence of these technologies on the nature of work in the future. Also, it focuses on the necessity for companies to create strategies that take advantage of both possibilities and problems. To effectively recruit and develop the talents necessary to merge human and machine efforts in modern workplaces, the H.R. role is growing.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Classification algorithms to predict the risk of fetal death in Ecuador
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Byron Albuja-Sanchez, Jeniffer Flores-Toala, Arcesio Bustos-Gaibor, Sandra Arias-Villon
Abstract - The problem with predicting a possible risk of fetal death is that it depends on several aspects, not only medical but also economic and social aspects of the pregnant mothers, which makes an early response to this problem very difficult. This work seeks to apply classification algorithms to detect the risk of fetal death based on socioeconomic and demographic data of pregnant mothers in Ecuador, using datasets from 2000 to 2021. Trained algorithms include decision trees, random forests, neural networks, bagging classifiers, k nearest neighbors, and naive bayes bernoulli. As cases of fetal death are very rare, over-sampling and undersampling techniques were applied to train the algorithms. The performance comparison of the trained algorithms was carried out with their respective confusion matrices. The best performance was obtained by the algorithms trained with undersampling and of all of them the performance of the neural network stood out. The best performance of the neural network was associated with its nature of classifying by assigning weights to each input parameter.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Deploying Large AI Models on Micro-Electronics with RISC-V: Federated Learning for Energy Monitoring and Robotics
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, George Dimitrakopoulos, Faycal Bensaali
Abstract - This paper introduces an innovative architecture for deploying AI models in edge and cloud environments, leveraging Federated Learning and RISC-V processors for privacy and real-time inference. It addresses the constraints of edge devices like Raspberry Pi and Jetson Nano by training models locally and aggregating results in the cloud to mitigate overfitting and catastrophic forgetting. RISC-V processors enable high-speed inference at the edge. Applications include energy consumption monitoring with LSTM models and recommendations via collaborative filtering, and multi-robot human collaboration using CNN and YOLO models. Model compression and partitioning optimize performance on RISC-V, with experiments demonstrating scalability and responsiveness under varying computational demands.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Generative AI for School Leaders
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Joyce Wong Ching Yan, Davy Tsz Kit Ng
Abstract - This chapter investigates the effects of generative artificial intelligence and digital transformation on K-16 school leaders in the post-pandemic period. It describes the challenges those leaders face in the technology integration processes and recommends developing AI competencies, interdisciplinary curricula and relevant leadership skills. The shift to learning over the Internet as a consequence of the actions that were taken towards the prevention of the spread of covid 19 brought many positives but many education managers continue to face the challenges of educational technology integration. Such factors include the influence of wonderful ideas and the provision of working rooms that encourage teacher collaboration. The chapter proposes plans for creating professional development furthering teachers’ modernization of their digital knowledge. It also addresses the aspect of the efficient digital design that helps implement contemporary curricular programs. As soon as they pay attention to these most important items, their schools will be more able to meet the needs of the digital world market, and consequently enhance the students’ performance. This chapter adds further discussion of initiatives and projects on school leadership and technology communication integration. It describes specific techniques relevant to today's education and the issues that they face as the environment changes rapidly.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Identifying Key Factors Influencing the Cost of Running Microservices
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Mohammad Hamzehloui, Ardavan Ashabi
Abstract - Microservices have emerged as a preferred architectural style for developing scalable and resilient applications, especially within cloud environments. This approach offers significant advantages over traditional monolithic architectures, such as enhanced scalability, flexibility, and fault isolation. However, these benefits come with substantial operational costs. Running microservices on cloud platforms incurs high expenses due to the need for extensive monitoring, complex service management, and dynamic resource allocation. Industry solutions have primarily focused on monitoring and management, leaving a gap in comprehensive strategies for cost reduction through optimization and resource management. This study aims to identify and analyze the primary cost drivers of running microservices and assess their individual impacts. By providing a detailed analysis, this research enhances the understanding of cost factors, aiding in the cost management and optimization of cloud-based microservices. This knowledge helps businesses make informed decisions to minimize expenses while maximizing the benefits of cloud adoption. Key cost drivers identified include virtualization mechanisms, scaling solutions, microservice architectures, API designs. Microservices vary significantly in terms of performance and resource consumption depending on their design and architecture. However, by following certain best practices, it is possible to reduce the overall running costs of microservices by minimizing resource consumption.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

MO-BMB for Multi-Objective Task Offloading Optimization in Fog-Cloud Environment
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Rachel Roux, Sonia Yassa, Olivier Romain
Abstract - Cloud computing is widely used to collect data from various devices, which must be processed quickly. To manage this growing data, fog computing helps reduce delay and processing costs by assigning tasks to suitable devices. This article presents an adapted binary monarch butterfly algorithm for task offloading in a fog-cloud environment. This metaheuristic directly constructs a Pareto front, offering a solution space representation. Two versions are examined: one using random search and the other a deterministic search with crowding distance. Simulations on tasks from 40 to 500 show that the binary Monarch Butterfly algorithm can outperform state-of-the-art algorithms for cost optimization while balancing delay.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

A Probabilistic Graphical Model for Concept Identification from Educational Documents
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Debjani Mazumder, Jiaul H. Paik, Anupam Basu
Abstract - The large volume of online educational materials makes it difficult for learners to find adequate resources for better learning. Understanding these materials relies on identifying key concepts essential for comprehension. Automatic concept extraction is an important task in educational data mining and is similar to keyphrase extraction in Natural Language Processing (NLP). This process helps identify key ideas, organize documents, and build an insightful learning path. We present a probabilistic approach for concept extraction. Candidate concepts are generated using Wikipedia anchor texts. We identify the necessary concepts based solely on the educational context of a particular document using a graph-based probabilistic model. Evaluation of our method on two datasets (namely, a Physics school textbook and Physics articles 3) outperforms existing unsupervised and supervised methods.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

DoS Attack Detection using a Machine Learning and Multi-Objective Optimization Approach in IoT Networks
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Erich Giusseppe Soto Parada, German A. Montoya, Carlos Lozano-Garzon
Abstract - The Internet of Things (IoT) is a fast-developing technological domain that has seen remarkable expansion in recent years; however, the security of these devices is critical, particularly with Denial of Service (DoS) attacks, which seek to overload a service or device by using a machine to render the device inactive. In this sense, we propose two machine learning approaches: a Random Forest approach, which has an F1 score of 0.99985 and an inference time of 0.457026 seconds for almost 500,000 records, and another from XGBoost, with an F1 score of 0.998989 and an inference time of 0.325767 seconds for the same 500,000 records. According to the data set, the methodologies used, and their results, these models were the most suitable for addressing the security issues imposed by DoS attacks.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Dynamic Handgun Detection with YOLOv11: From Images to Real-Time CCTV Monitoring
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Wirote Jongchanachavawat, Nirumol Hirunwijitporn, Noppon Mingmuang, Pisit Plaikaew, Supachai Poumpong, Narongsak Wornplop, Pannawat Koonmee
Abstract - The increasing prevalence of firearms poses significant challenges to public safety, particularly in high-risk environments such as schools, airports, and transportation hubs. This study explores the implementation and performance of YOLOv11, the latest advancement in the YOLO series, for handgun detection in static images, video streams, and real-time CCTV monitoring. By leveraging its transformer-based architecture and adaptive scene understanding, YOLOv11 achieves exceptional accuracy, low latency, and minimal false positives across diverse scenarios. The results demonstrate YOLOv11's superiority over previous iterations in precision, speed, and robustness, making it a reliable solution for real-time threat detection. This research underscores the potential of integrating YOLOv11 into modern surveillance systems to enhance public safety and crime prevention efforts.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Integration of Classification, Regression and Ranking Tasks into a Novel Multi-Tasks SVM for Stock Market Prediction
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Pei-Yi Hao
Abstract - Most stock prediction models rely on classification or regression methods to forecast stock price trends or prices, with their primary goal being to enhance the fit between predicted results and actual values rather than directly identifying the best investment targets. Consequently, the stocks recommended by these models may not necessarily yield the optimal returns. In contrast, stock ranking prediction provides a more direct and effective approach to portfolio construction by forecasting the ranking sequence of stock returns (with higher-return stocks ranked higher). This process is referred to as stock selection. The key to stock selection lies in identifying stocks that are most likely to help investors generate profits. Since stock prediction involves different tasks such as classification, regression, and ranking, which exhibit significant interrelations, most deep learning algorithms tend to train these tasks independently, overlooking their correlations. However, these related tasks may share underlying knowledge, which should be jointly learned to maximize the utilization of the potential information behind each task. Support vector machines (SVMs) have demonstrated exceptional performance in multi-task learning and have achieved success in numerous practical applications. This paper proposes a novel multitask support vector machine capable of simultaneously learning classification, regression, and ranking models. By leveraging the correlations among these tasks, the proposed framework aims to improve the predictive performance of each individual task.
Paper Presenters
avatar for Pei-Yi Hao
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Modern Approaches to Learning Assessment in Online Education: Bridging Traditional and Innovative Practices
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Paula Escudeiro, Marcia Campos Gouveia, Nuno Escudeiro
Abstract - The modern era is characterized by technological advancements, societal changes, and a reassessment of long-held paradigms. Within this shifting landscape, approaches to teaching and learning assessment have undergone substantial transformation. Modern pedagogical practices focus on understanding how students learn rather than merely assessing what they learn. Evaluating progress in online courses requires continuous assessment strategies that uphold the same level of credibility as traditional, face-to-face evaluations. The integration of quantitative and qualitative models, along with self-assessment and peer assessment, is vital for ensuring robust and effective evaluation in online learning environments.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Optimization of Predictive Models in Breast Cancer: Applications and Advances in Feature Selection
Wednesday February 19, 2025 9:30am - 11:00am GMT
Authors - Ainhoa Osa-Sanchez, Paulina Carcamo Ibarra, Begonya Garcia Zapirain
Abstract - Breast cancer remains one of the most diagnosed cancers and a leading cause of cancer-related mortality worldwide. Advances in predictive modeling have introduced innovative methods to improve breast cancer prognosis and recurrence prediction, particularly through the integration of clinical, radiomic, and temporal data. This study focuses on the application of advanced feature selection techniques and machine learning algorithms, including Random Forest, XGBoost, and Lasso Regression, to optimize the performance and interpretability of predictive models. Radiomic features, such as the median of intensity histogram and the difference entropy of the grey level co-occurrence matrix (GLCM), alongside clinical and temporal variables, were identified as key predictors of recurrence. Our findings underscore the potential of combining multimodal data with robust feature selection techniques to enhance personalized treatment strategies for HER2-positive breast cancer patients. Future research should address dataset generalizability and incorporate multi-omics data to further refine these predictive approaches.
Paper Presenters
Wednesday February 19, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

11:00am GMT

Session Chair Remarks
Wednesday February 19, 2025 11:00am - 11:03am GMT
Wednesday February 19, 2025 11:00am - 11:03am GMT
Virtual Room A London, United Kingdom

11:00am GMT

Session Chair Remarks
Wednesday February 19, 2025 11:00am - 11:03am GMT
Wednesday February 19, 2025 11:00am - 11:03am GMT
Virtual Room B London, United Kingdom

11:00am GMT

Session Chair Remarks
Wednesday February 19, 2025 11:00am - 11:03am GMT
Wednesday February 19, 2025 11:00am - 11:03am GMT
Virtual Room C London, United Kingdom

11:00am GMT

Session Chair Remarks
Wednesday February 19, 2025 11:00am - 11:03am GMT
Wednesday February 19, 2025 11:00am - 11:03am GMT
Virtual Room D London, United Kingdom

11:03am GMT

Closing Remarks
Wednesday February 19, 2025 11:03am - 11:05am GMT
Wednesday February 19, 2025 11:03am - 11:05am GMT
Virtual Room A London, United Kingdom

11:03am GMT

Closing Remarks
Wednesday February 19, 2025 11:03am - 11:05am GMT
Wednesday February 19, 2025 11:03am - 11:05am GMT
Virtual Room B London, United Kingdom

11:03am GMT

Closing Remarks
Wednesday February 19, 2025 11:03am - 11:05am GMT
Wednesday February 19, 2025 11:03am - 11:05am GMT
Virtual Room C London, United Kingdom

11:03am GMT

Closing Remarks
Wednesday February 19, 2025 11:03am - 11:05am GMT
Wednesday February 19, 2025 11:03am - 11:05am GMT
Virtual Room D London, United Kingdom

11:43am GMT

Opening Remarks
Wednesday February 19, 2025 11:43am - 11:45am GMT
Wednesday February 19, 2025 11:43am - 11:45am GMT
Virtual Room A London, United Kingdom

11:43am GMT

Opening Remarks
Wednesday February 19, 2025 11:43am - 11:45am GMT
Wednesday February 19, 2025 11:43am - 11:45am GMT
Virtual Room B London, United Kingdom

11:43am GMT

Opening Remarks
Wednesday February 19, 2025 11:43am - 11:45am GMT
Wednesday February 19, 2025 11:43am - 11:45am GMT
Virtual Room C London, United Kingdom

11:43am GMT

Opening Remarks
Wednesday February 19, 2025 11:43am - 11:45am GMT
Wednesday February 19, 2025 11:43am - 11:45am GMT
Virtual Room D London, United Kingdom

11:43am GMT

Opening Remarks
Wednesday February 19, 2025 11:43am - 11:45am GMT
Wednesday February 19, 2025 11:43am - 11:45am GMT
Virtual Room E London, United Kingdom

11:45am GMT

A Novel One-Dimensional Approach to Human Activeness Measurement Using a Single PIR Sensor
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Tajim Md. Niamat Ullah Akhund, Kenbu Teramoto
Abstract - The demand for efficient human activity recognition systems has surged recently, driven by the need for intelligent monitoring in various environments such as smart homes and workplaces. This paper presents a novel approach to measuring human activeness using a single Passive Infrared (PIR) sensor, highlighting its simplicity, costeffectiveness, and privacy-conscious design. This paper introduces a novel one-dimensional modeling approach for measuring human activeness using a single Passive Infrared (PIR) sensor, incorporating the Laplace distribution to analyze movement patterns. We define an activeness index μ, quantifying average human activity over time, allowing for precise numerical assessment. Our method utilizes the sensor’s capabilities to gather data on human movement and generate numerical metrics of average activeness over time. The results demonstrate that this approach effectively captures human activity levels while minimizing equipment complexity. This work contributes to the growing field of human activity recognition by offering a practical solution that balances performance with user privacy and affordability.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Obesity Level Prediction Using Machine Learning
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Hasti Vakani, Mithil Mistry, Hardikkumar Jayswal, Nilesh Dubey, Nitika Sharma,Rohan Patel, Dipika Damodar
Abstract - Obesity has become a significant global health concern due to its as-sociation with various non-communicable diseases. Traditional methods for obesity assessment, such as BMI, often fail to capture the complexity of the condition, highlighting the need for more accurate predictive tools. This research utilize the machine learning algorithms, including Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks, in a stacking ensemble model to predict obesity levels. Utilizing datasets from diverse populations, the model achieved a high accuracy of 96.69%. Key features such as BMI, age, and dietary habits were identified as critical predictors through Recursive Feature Elimination. The research findings demonstrate the potential of advanced data-driven techniques in providing personalized insights into obesity management and underscore the transformative role of machine learning in public health initiatives.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Obesity Risk Prediction Using Machine Learning by Combining Lifestyle Factors and Social Media Behavior
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Kutub Thakur, Md Liakat Ali, Suzanna Schmeelk, Joan Debello, Denise Dragos
Abstract - The escalating prevalence of obesity in young adults has become a pressing public health concern, requiring innovative risk prediction and intervention approaches. This paper examines the potential of combining traditional lifestyle factors with social media behavior to predict obesity risk in young adults while addressing ethical considerations related to data privacy and informed consent. By identifying the most predictive social media metrics associated with obesity risk, this research offers novel insights that could inform targeted prevention strategies. Through a mixed-methods approach, the study examines the associations between social media behavior, traditional lifestyle factors, and obesity risk while ensuring adherence to ethical guidelines and protecting individual privacy. The findings highlight the importance of integrating social media metrics into risk prediction models, offering new avenues for intervention and prevention efforts. This research provides a deeper understanding of the complex interplay between social media behavior, lifestyle factors, and obesity risk, emphasizing the need for multidisciplinary approaches to tackle this growing public health challenge.
Paper Presenters
avatar for Kutub Thakur

Kutub Thakur

United States of America
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Predicting Risk of Future Hospitalizations in Patients with Type 2 Diabetes Mellitus and Cardiovascular Diseases using Machine Learning
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Alisher Ikramov, Shakhnoza Mukhtarova, Raisa Trigulova, Dilnoza Alimova, Dilafruz Akhmedova
Abstract - Hospital readmissions pose a significant burden on healthcare systems, especially for patients with type 2 diabetes mellitus (T2DM) and cardiovascular diseases. Early readmission risk prediction is crucial for improving patient outcomes and reducing costs. In this study, we develop a predictive model based on accessible clinical features to estimate the risk of future hospitalizations. Using data from 260 patients at the Republican Specialized Scientific and Practical Medical Center for Cardiology in Uzbekistan, we trained a Generalized Linear Model that achieved a ROC AUC of 0.898 on the test set.
Paper Presenters
avatar for Alisher Ikramov

Alisher Ikramov

Uzbekistan
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

UAV path planning for enhanced connectivity in environments with limited infrastructure using DDQN
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Lucas V. Santos, Vitor B. Souza
Abstract - Fog computing emerges as an innovative solution for edge data processing, proving to be particularly important in the context of the Internet of Things (IoT) by delivering low latency and high bandwidth at the cost of requiring a stable connection. One application that has greatly benefited from this concept is the use of Unmanned Aerial Vehicles (UAVs), also known as drones, for various applications requiring real-time communication between these devices and, potentially, a base station. This paper focuses on the use of UAVs, highlighting the connectivity challenges posed by the limitations of wireless communication technologies, such as Wi-Fi. To address these challenges, we propose a model based on deep reinforcement learning (DDQN), which helps drones make decisions on the best route between the origin and destination, balancing the minimization of travel time and the maximization of connectivity throughout the journey. Using a simulated environment where drones are trained to avoid disconnection areas, we found that the proposed model significantly improves connection stability in areas with limited coverage, albeit with an unavoidable increase in route distance. Comparisons with traditional routing methods demonstrate the advantages of our model.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Virtual Customer Service Assistant for University Students
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Sacrificio Sithole Junior, Mohammad Gulam Lorgat
Abstract - The increase in the number of university students has resulted in long queues and delays in services, both during orientation events and in resolving general queries. A service chatbot is an artificial intelligence tool designed to interact with users, answering frequently asked questions and assisting in solving problems in an automated and efficient manner. This study presents the development of a chatbot prototype for the Faculty of Engineering administrative office in Chimoio, at the Universidade Católica de Moçambique (UCM), aiming to optimise service delivery, reduce waiting times, and increase efficiency in resolving common issues. Using a mixed-method approach, the study involved direct observation and questionnaires administered to students to identify the main problems with traditional service. The chatbot's development was carried out in two phases: the first involved data collection and the identification of needs, while the second covered the implementation of the prototype. This chatbot can provide a viable and effective solution to the challenges faced, delivering faster and more efficient service, while freeing up human resources for more complex tasks.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Automated Detection of Potholes and Speed Bumps Using Deep Learning
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Mithil Mistry, Hasti Vakani, Hardikkumar Jayswal, Nilesh Dubey, Mann Patel, Jai Mehtani, Dipika Damodar, Ayush kariya
Abstract - This research explores the integration of advanced deep learning techniques for automated pothole and speed bump detection, highlighting various methodologies from recent literature. A systematic review reveals that models utilizing Convolutional Neural Networks (CNNs), including YOLOv3, VGG16, and EfficientNetB0, have achieved impressive accuracy rates in real-time road surface monitoring. In particular, the EfficientNetB0 model was finetuned using a comprehensive dataset comprising 400 annotated images of potholes and speed bumps, collected under diverse environmental conditions. The model achieved a validation accuracy of 91.91%, demonstrating robust performance in identifying road anomalies. Notably, the implementation of advanced data augmentation and regularization techniques, such as dropout and L2 regularization, contributed to preventing overfitting and enhancing generalization across varying contexts. This study underscores the potential of deep learning frameworks in improving road safety and maintenance efficiency, paving the way for future enhancements, including dataset expansion and real-time application integration.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Detecting Satire in Multimodal News Content
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Ishaan Bhattacharjee, Pranav H P, Harish Satheesh, Disha Jain, Bhaskarjyoti Das
Abstract - Satirical content is notoriously difficult to detect, even humans often struggle to discern satire from genuine news. While significant strides have been made in computationally modeling textual satire using supervised learning, the challenge of detecting satire in multimodal content—combining both text and images—remains largely unexplored. In our research, we aim to address this gap by leveraging existing frameworks and tools to detect and differentiate multimodal satire from true news content. Satire builds on two key factors, i.e., knowledge and incongruity. Knowledge has two parts, i.e., local knowledge that is resident in image and text and global contextual knowledge that is not part of the content. Incongruity typically occurs between the first and second parts of the text. In this work, we present a three-step framework. First, we investigate multimodal frameworks such as BLIP, relying on its global knowledge without explicitly modeling the incongruity. Second, we attempt to model incongruity by focusing on the semantic gap between two parts of the text content while using a large language model in knowledge enhancement and next-sentence prediction. Finally, we combine the above two models utilizing local knowledge, global knowledge, and incongruity to offer class-leading performance. The investigations described in this work offer novel insights into the detection of satire in complex, multimodal content.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Implementation of Motivational Qualities within Serious Game Development
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - James Uys, Gunther Drevin, Lynette Drevin
Abstract - Serious games have seen a rise in popularity as an alternative method to deliver information to learners. A problem that is often faced is maintaining learner engagement during the educational process. To address this, it is important to identify the elements which are essential to keeping a learner motivated during the learning experience. This research focused on motivational drivers behind learning as well as prevalent characteristics in serious games. These elements were then integrated into the development life cycle of a serious game. The developed artefact was evaluated using the RETAIN model.
Paper Presenters
avatar for James Uys

James Uys

South Africa
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

The reskilling of coal miners in the digital age: A quantitative study
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Lloyd L.K. Modimogale, Jan H. Kroeze, Corne J. van Staden
Abstract - The paper presents the results of a quantitative study examining the impact of the Fourth Industrial Revolution (4IR) on the South African coal mining sector, specifically focusing on reskilling. As mechanization and digital technologies increasingly permeate the industry, this study investigates the implications for employment and skill requirements among coal miners. Data on job displacement rates, skill sets, and reskilling initiatives within the coal mining workforce were collected using statistical analysis. The findings indicate a significant decline in demand for traditional low-skilled job roles, highlighting the urgent need for reskilling programs to facilitate workforce adaptation to new technological demands. The study aims to understand how coal miners view the impact of digitalization on the coal mine and the management of the reskilling process. The research highlights the need for proactive measures to mitigate job insecurity and ensure that workers remain relevant in a rapidly changing economic environment, thus contributing to the broader discourse on sustainable labor practices within the mining sector.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

The role of influencers in the information consumption of young people in Portugal
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Mafalda Reis, Lidia Oliveira, Catarina Feio
Abstract - The main objective of this research is to study the role of online influencers in the information consumption of young people in Portugal. It studies the information consumption habits of young people aged between 18 and 30, as well as their views on the role of influencers in transmitting information on social media and their relationship with the concept of opinion leadership. A literature review was conducted since 2018 to generate a view of the state of the art on the issue under study. The data collection instrument was a questionnaire survey, obtained using an interpretative methodology, through a quantitative analysis of the data. From the analysis of the 322 respondents to the questionnaire, it was concluded that: young people consume more information on social networks; women and students are the ones who follow influencers the most; the relationship between influencers and followers is not one of friendship; young people are mainly interested in health issues and national news; young people have already learned about a current issue because an influencer talked about it; young people consider influencers to be active members of a given online community.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Unveiling Animal Emotions: A Deep Learning Approach with Explainable AI for Emotion Detection
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Kornprom Pikulkaew, Apinantn Sumthumpruek
Abstract - Investigating the emotional lives of animals is inherently complicated although the findings are rewarding in terms of conservation, psychology, and sociopsychology. In this paper, we consider the problem of animal emotion detection with the aid of deep learning using dogs' emotions in this study. In this case, four categories of emotions, angrily, joyfully, relaxed, and sadly were classified based on the best CNNs such as ResNet-50, EfficientNet, and MobileNet. Furthermore, by employing data preprocessing, data augmentation techniques, and structural explainability techniques like the Grad-CAM, we could improve how the model made critical decisions. Results confirmed that the model per-formed satisfactorily in detecting the subjects' emotions even though joyful and relaxed states were more pronounced with high levels of accuracy compared to others with emotions like sadness and anger lauded as a notable challenge, especially in discriminating attitudes that seemed too close to one another. The application of Grad-CAM was able to elaborate on the regions of interest incorporated by the model thus enhancing the explainability of the model. This paper is focused on the emergent developments in emotion detection in animals and further recommends for advancement of three-dimensional deep learning techniques, settlement of the dataset, and the introduction of more complicated explainable AI techniques such as Local Interpretable Model Explanations (LIME).
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Blockchain-enabled smart contract adoption in infrastructure PPP projects: understanding the driving forces within the TOE framework
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors -
Abstract -
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Enhanced Aerial Scene Classification Through ConvNeXt Architectures and Channel Attention
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Leo Thomas Ramos, Angel D. Sappa
Abstract - This work explores the integration of a Channel Attention (CA) module into the ConvNeXt architecture to improve performance in scene classification tasks. Using the UC Merced dataset, experiments were conducted with two data splits: 50% and 20% for training. Models were trained for up to 20 epochs, limiting the training process to assess which models could extract the most relevant features efficiently under constrained conditions. The ConvNeXt architecture was modified by incorporating a Squeeze-and-Excitation block, aiming to enhance the importance of each feature channel. ConvNeXt models with CA showed strong results, achieving the highest performance in the experiments conducted. ConvNeXt large with CA reached 90% accuracy and 89.75% F1-score with 50% of the training data, while ConvNeXt base with CA achieved 77.14% accuracy and 75.23% F1-score when trained with only 20% of the data. These models consistently outperformed their standard counterparts, as well as other architectures like ResNet and Swin Transformer, achieving improvements of up to 9.60% in accuracy, highlighting the effectiveness of CA in boosting performance, particularly in scenarios with limited data.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Geo-spatial and Temporal Analysis of Hadith Narrators
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Hamada R. H. Al-Absi, Devi G. Kurup, Amina Daoud, Jens Schneider, Wajdi Zaghouani, Saeed Mohd H. M. Al Marri, Younss Ait Mou
Abstract - This study integrates traditional Science of Hadith literature —documenting the sayings, actions, and approvals of the Prophet Muhammad (PBUH) with modern digital tools to analyze the geographic and temporal data of Hadith narrators. Using the Kaggle Hadith Narrators dataset, we apply Kernel Density Estimation (KDE) to map the spatial distribution of narrators’ birthplaces, places of stay, and death locations across generations, revealing key geographical hubs of Hadith transmission, such as Medina, Baghdad, and Nishapur. By examining narrators’ timelines and locations, we illustrate movement patterns and meeting points over time, providing insights into the spread of Hadith across the Islamic world during early Islamic history. To our knowledge, this research is the first systematic attempt to analyze Hadith transmission using geo-spatial and temporal methods, offering a novel perspective on the geographic and intellectual dynamics of early Islamic scholarship.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Joined Video Description from Multiple Sources
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Francisco Seipel-Soubrier, Jonathan Cyriax Brast, Eicke Godehardt, Jorg Schafer
Abstract - We propose an architecture of a proof-of-concept for automated video summarization and evaluate its performance, addressing the challenges posed by the increasing prevalence of video content. The research focuses on creating a multi-modal approach that integrates audio and visual analysis techniques to generate comprehensive video descriptions. Evaluation of the system across various video genres revealed that while video-based large language models show improvements over image-only models, they still struggle to capture nuanced visual narratives, resulting in generalized output for videos without a strong speech based narrative. The multi-modal approach demonstrated the ability to generate useful short summaries for most video types, but especially in speech-heavy videos offers minimal advantages over speech-only processing. The generation of textual alternatives and descriptive transcripts showed promise. While primarily stable for speech-heavy videos, future investigation into refinement techniques and potential advancements in video-based large language models holds promise for improved performance in the future.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Optimized Edge AI Framework with Image Processing for Speed Prediction in Semi-Automated Electric Vehicles
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - A.G.H.R. Godage, H.R.O.E. Dayaratna
Abstract - This study explores the implementation of edge computing for semi-automated vehicle systems in urban environments, leveraging modern wireless technologies such as 5G for efficient data transmission and processing. The proposed framework integrates a vehicle-mounted camera, an edge server, and deep learning models to identify critical objects, such as pedestrians and traffic signals, and predict vehicle speeds for the subsequent 30 seconds. By offloading computationally intensive tasks to an edge server, the system reduces the vehicle’s processing load and energy consumption, while embedded offline models ensure operational continuity during network disruptions. The research focuses on optimizing image compression techniques to balance bandwidth usage, transmission speed, and prediction accuracy. Comprehensive experiments were conducted using the Zenseact Open Dataset, a new dataset published in 2023, which has not yet been widely utilized in the domain of semi-automated vehicle systems, particularly for tasks such as predictive speed modeling. The study evaluates key metrics, including bandwidth requirements, round-trip time (RTT), and the accuracy of various machine learning and neural network models. The results demonstrate that selective image compression significantly reduces transmission times and overall RTT without compromising prediction quality, enabling faster and more reliable vehicle responses. This work contributes to the development of scalable, energy-efficient solutions for urban public transport systems. It highlights the potential of integrating edge AI frameworks to enhance driving safety and efficiency while addressing critical challenges such as data transmission constraints, model latency, and resource optimization. Future directions include extending the framework to incorporate multi modality, broader datasets, and advanced communication protocols for improved scalability and robustness.
Paper Presenters
avatar for A.G.H.R. Godage
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Semantic Landscape of Legal Lexicons: Unpacking Medical Decision Making Controversies
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Haesol Kim, Eunjae Kim, Sou Hyun Jang, Eun Kyoung Shin
Abstract - This study empirically examined historical trajectories of the semantic landscape of legal conflicts over medical decision making. We unveiled the lexical structures of lawsuit verdicts, tracing how the core concepts of shared decision making (SDM)-duty of care, duty to explain, self-determination-have developed and been contextualized in legal discourses. We retrieved publicly available court verdicts using the search keyword ‘patient’ and screened them for relevance to doctor-patient communications. The final corpus comprised 251 South Korean verdicts issued between 1974 and 2023. We analyzed the verdicts using neural topic modeling and semantic network analysis. Our study showed that topic diversity has expanded over time, indicating increased complexity of semantic structures regarding medical decision-making conflicts. We also found two dominant topics: disputes over healthcare providers’ liability and disputes over the compensation for medical malpractice. The results of semantic network analysis showed that the rhetorics of patients’ right to medical self-determination are not closely tied to the professional responsibility to explain and care. The decoupled semantic relationships of patients’ right and health professionals’ duties revealed the barriers of SDM implementations.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Bridging Simulation and Reality: A Digital Twin Approach for UAVs
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Bryan S. Guevara, Jose Varela-Aldas, Viviana Moya, Daniel C. Gandolfo, Juan M. Toibero
Abstract - This study presents an innovative methodology for the development and testing of a digital twin for an Unmanned Aerial Vehicle (UAV), effectively bridging the simulation-reality gap. The proposed approach integrates Model-in-the-Loop (MiL) and Hardware-in-the-Loop (HiL) testing, enabling a comprehensive evaluation of the UAV’s digital twin in simulated environments. Behavioral testing includes open-loop scenarios, baseline feedback controllers, and Model Predictive Control (MPC). The UAV’s dynamic model is simplified and rigorously validated through experimental verification, ensuring high fidelity and reliability. Furthermore, this approach facilitates the critical transition from simulation to real-world experimentation by providing a robust framework for evaluating UAV performance under realistic conditions. This methodology highlights the importance of experimental validation in replicating real-world scenarios, ensuring the robustness and accuracy of the digital twin.
Paper Presenters
avatar for Viviana Moya
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Exploring Usability of AR Budur: A Study with Gen Z Users
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Restyandito, Danny Sebastian, Audrianne Gunawan
Abstract - Borobudur Temple, a 9th-century Mahayana Buddhist monument in Central Java, Indonesia, is renowned for its intricate design and historical significance, attracting millions of visitors annually. Generation Z, being digital natives, prefers interactive and personalized experiences, making the AR Budur application an ideal solution for navigating the vast and intricate Borobudur Temple complex. By leveraging Augmented Reality, AR Budur aims to provide an engaging and intuitive experience that aligns with the tech-savvy nature of Generation Z, enhancing their exploration of this culturally significant site. Based on the research results Generation Z, especially female high school and undergraduate students, are ideal candidates for the AR Budur application due to their tech-savviness and high smartphone usage. Their navigation preferences emphasize the need for combining digital tools with human assistance and features like street view and real-time updates. Usability testing of AR Budur showed an average SUS score of 70.357, indicating above-average usability but with room for improvement. Feedback highlighted issues like elevation discrepancies in 3D mapping, which have been addressed, and suggestions for adding information about Borobudur Temple's reliefs for future updates.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Information and Communication Technology (ICT) and Data-Driven Approaches for Mid-Term Plans: Development of Strategic Management Framework in Higher Education
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Kunihiko Takamatsu, Tetsuya Oishi, Takahiro Seki, Kahori Ogashiwa
Abstract - This study investigates the transformation of higher education in the post-COVID-19 era, focusing on the integration of Information and Communication Technology (ICT) and data-driven education approaches in strategic planning processes. Through a comprehensive survey of Japanese universities (N=816), including national (52.3% response rate), public (42.2%), and private institutions (15.8%), we examined how universities are adapting their medium-term planning frameworks to address contemporary challenges. The research reveals that 98.4% of respondents recognize the importance of data utilization in strategic planning, while highlighting the need for a more sophisticated approach that combines traditional methods with Eduinformatics frameworks. Our findings demonstrate that the post-COVID-19 landscape demands strategic plans that effectively leverage both quantitative metrics and qualitative assessments, particularly in evaluating educational outcomes. The study identifies key challenges in implementing data-driven approaches and proposes a comprehensive model for strategic planning that integrates ICT capabilities with institutional research (IR) methodologies. This research contributes to the emerging field of Eduinformatics by providing empirical evidence for the development of adaptive, technology-enhanced planning frameworks in higher education, while acknowledging the need for flexible, institution-specific approaches to strategic management in the post-COVID-19 era.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Insider Threat Prediction using Machine Learning Techniques: A Literature Review
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Ashok Kumar, Sanjeev Patwa, Sunil Kumar Jangir
Abstract - Insider threats are one of the toughest challenges in cybersecurity. Insider attacks can be particularly dangerous because they often go unnoticed and can lead to serious problems like data breaches, financial losses, and damage to a company’s reputation. This issue has become even more pressing in recent years with the rise of digital operations and remote work. Research-ers have shown how machine learning can help predict these insider threats. While supervised learning models have shown great accuracy in identifying threats in certain datasets, they face a major hurdle: there simply isn’t enough labeled data on insider threats. On the other hand, unsu-pervised learning methods can spot unusual behavior and reveal hidden threats, but they often produce false alarms. Deep learning techniques could potentially offer better accuracy, but they require a lot of computing power and large amounts of training data. There are also exciting new trends in the field, such as behavioral biometrics, hybrid models, and explainable AI. However, challenges like inconsistent evaluation metrics and the difficulty of applying these models across different organizations still exist. This review aims to bring together existing research and pin-point key areas that need more attention, providing a roadmap for future studies. By addressing issues like the need for standardized datasets, encouraging collaboration across different fields, and incorporating contextual data from organizations, this paper seeks to help future researchers create more effective and adaptable models for predicting insider threats.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Killing Two Birds With One Stone: The Study of User Engagement Influencing the Job Application Process
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Key Sabbathany Togiana Tambunan, Nurul Sukma Lestari, Tri Wiyana
Abstract - This study aims to analyze the relationship between Perceived Usability, Perceived Information, Perceived Service interaction, Satisfaction, and Engagement among the associates and the organization to which they apply. The methodology in this research paper involved quantitative data collected from a sample of 200 associates working within luxurious five-star hotels in Jakarta that implement online application platforms in the job application process. The data processed utilized the SEMPLS. The findings have revealed that a higher level of Perceived Usability, clear Perceived Information, and Perceived Service Interaction unquestionably contributes to an increase in satisfaction and engagement among candidates. The novel perspective of this study contributes by associating Perceived Information and Perceived Service Interaction directly to user engagement in Jakarta’s luxury hotels, by offering an insight that management is encouraged to prosecute during the job application process. This study discovers the new finding of the need for change to a wider range of comprehension of how clear communication and relevant information mold the user experience. Focusing on the experience of users in the application platforms, this research provides a unique understanding for management to increase the success of recruitment and nurture an engaging work environment for potential associates. Future studies are encouraged to explore these dynamics through extensive research to achieve a better understanding of their long-term impact on employee performance and retention in the fast-changing hospitality industry.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Revisiting Communication Theories in Human-Machine Communication: The Joint Sense-Making Process between Humans and Machines
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Xuening Tang
Abstract - Human-Machine Communication (HMC) explores the joint meaning-making processes between humans and communicative intelligent machines, as well as its broader societal implications. The rapid advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG) have transformed intelligent machines from passive tools to active social actors, capable of understanding and generating human-like messages. This paper evaluates the evolving research agenda of HMC, focusing on popular communication frameworks such as the CASA paradigm, the dual-process model, and social presence. It examines their theoretical foundations, transformations, limitations, and potential future extensions. Furthermore, this paper discusses the societal and ethical dimensions of HMC and provides recommendations for advancing research, including interdisciplinary approaches and the refinement of methodologies.
Paper Presenters
avatar for Xuening Tang

Xuening Tang

Netherlands
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Alzheimer Diagnosis through Advanced Deep Learning Architectures and Interpretative Analysis of Predictions
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Jorge Lituma, Anthony Moya, Remigio Hurtado
Abstract - Dementia, a critical global health challenge recognized by the World Health Organization (WHO), affects millions of lives, with more than 50 million cases reported in 2019, a figure projected to double by 2050. Among its forms, Alzheimer’s disease is the most prevalent, underscoring the urgent need for early detection to improve patient outcomes and mitigate societal impact. Leveraging recent advancements in artificial intelligence, this study introduces an innovative deep learning framework aimed at revolutionizing the diagnostic process, providing valuable insights for the scientific community and practical tools for medical professionals. The proposed approach is structured into five key phases: data collection, preprocessing, model training using transfer learning, quality metrics validation including Accuracy, Precision, Recall, and F1-Score—and result interpretation through integrated gradients. A robust dataset of over 40,000 MRI images was utilized, achieving an exceptional accuracy of 99.86% in classifying the stages of Alzheimer’s disease. To ensure interpretability, integrated gradients were employed to highlight critical neuroanatomical markers, such as cortical atrophy and enlarged ventricles, distinguishing patients with dementia from healthy individuals. These findings validate the model’s reliability and demonstrate its potential as an innovative tool for advancing Alzheimer’s diagnosis and care.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Auto Labelling of Vessel Trajectories for Maritime Downstream Tasks
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Douglas Amobi Amoke, Yichun Li, Syed Mohsen Naqvi
Abstract - Adopting machine learning solutions for monitoring vessel behaviour and surveillance in the maritime domain shows excellent promise. However, significant challenges arise due to the lack of publicly available vessel trajectory data labelled with Automatic Identification System (AIS) information. A new automated system has been proposed to preprocess and label vessel trajectory data collected from AIS at the Port of New York (NY), Blyth Port in Newcastle (NCL), United Kingdom, and a combined dataset called NYCL to address the labelling problem. This automated labelling system functions in three key stages. The first stage involves data collection and processing. The second stage transforms raw AIS data into meaningful vessel trajectory information. The third stage annotates and labels these trajectories, concluding with classification. The processed AIS data create vessel trajectories, with labels automatically generated. Finally, this work explores the classification models to demonstrate the effectiveness of labelled vessel trajectories in various maritime tasks.
Paper Presenters
avatar for Douglas Amobi Amoke

Douglas Amobi Amoke

United Kingdom
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Development of a Waste Bank Application with Real-Time Monitoring Dashboard for Sustainable Waste Management
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - MS Hasibuan, R Rizal Isnanto, Suryatiningsih, Chae Min A, Lee Kyung Min, Park So Hyeong
Abstract - This study aims to design and implement a waste bank application to improve waste management efficiency through digital solutions. The application provides a dashboard to track waste collection activities in real-time, displaying data on waste amounts, schedules, and user contributions, enhancing transparency and efficiency. Test results show the system improves waste bank operations by 25% and simplifies waste management reporting.
Paper Presenters
avatar for MS Hasibuan

MS Hasibuan

Indonesia
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Digital Innovation in MSMEs through Pentahelix Collaboration for Tourism Development
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Levyta Farah, Nurul Sukma Lestari, Dendy Rosman, Dewi Andriani
Abstract - MSMEs (Micro, Small, and Medium Enterprises) and tourism have a very close relationship and support each other. The collaboration between the two has great potential in improving the economy and regional development. Therefore, active collaboration is needed between tourist destinations and MSMEs in the regions to support each other and enhance the quality of tourism in Indonesia. This research investigates the influence of digital innovation and sustainable strategies on MSME performance with the Penta helix as a moderating variable. The population of this research is MSMEs in Tangerang City, with a sample size of 303 respondents. The results of this research are that digital innovation does not affect MSME performance, while sustainability strategy and Penta Helix have a positive effect on MSME performance. This research also shows that Penta Helix can moderate digital innovation and sustainability strategies on performance. This research clarifies the contribution of variables to the growth and sustainability of MSMEs, strengthens their position in the global market, and enables the development of more robust policies and business practices, potentially significantly contributing to overall economic growth and supporting tourism in the Tangerang area.
Paper Presenters
avatar for Levyta Farah

Levyta Farah

Indonesia
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Integration of a Solar-Powered Raspberry Pi System with an Embedded TFLite Model for Rice Leaf Disease Detection
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Anamika Dhawan, Pankaj Mudholkar
Abstract - Precision Agriculture has put in a lot of enhancement in improving agriculture in the last two decades. Plant monitoring is one of the essential applications of Precision Agriculture. In this study, an IoT-based system for rice leaf disease detection that runs on solar power and makes use of integrated machine learning on a Raspberry Pi 4 Model B is presented. In the classification of two important rice diseases, bacterial leaf blight and rice blast, the built custom Convolutional Neural Network (CNN) model, which was translated to TensorFlow Lite (TFLite) format for edge deployment, obtained a remarkable 94.28% accuracy. For scalable, effective disease detection in rice farming, this solar-powered, cost-effective device integrates edge AI and IoT.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Optimizing XGBoost Hyperparameter Selection with a Modified Metaheuristic: Applications in IoT Security
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Authors - Luka Jovanovic, Aleksandar Petrovic, Milan Tuba, Miodrag Zivkovic, Eva Tuba, Nebojsa Bacanin
Abstract - Strong security measures are required due to the growing use of IoT devices and constantly growing network sizes. In order to tackle some of the most important issues in IoT security, this paper investigates the use of optimization metaheuristics in XGBoost hyperparameter tuning. In particular, we suggest a brand-new modified metaheuristic algorithm that is intended to improve diversity throughout the search process and is modeled after the firefly algorithm (FA). Experiments with simulations on a newly released IoT security dataset show how well the proposed optimizer works to enhance model performance. While tackling important issues related to hyperparameter optimization, such as striking a balance between exploration and exploitation, the method achieves a noteworthy accuracy of 0.996853. These findings demonstrate how the suggested approach may strengthen network security by using more accurate predictive modeling, opening the door for scalable and effective IoT systems in progressively complex settings.
Paper Presenters
Wednesday February 19, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

1:15pm GMT

Session Chair Remarks
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Virtual Room A London, United Kingdom

1:15pm GMT

Session Chair Remarks
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Virtual Room B London, United Kingdom

1:15pm GMT

Session Chair Remarks
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Virtual Room C London, United Kingdom

1:15pm GMT

Session Chair Remarks
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Virtual Room D London, United Kingdom

1:15pm GMT

Session Chair Remarks
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Wednesday February 19, 2025 1:15pm - 1:17pm GMT
Virtual Room E London, United Kingdom

1:17pm GMT

Closing Remarks
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Virtual Room A London, United Kingdom

1:17pm GMT

Closing Remarks
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Virtual Room B London, United Kingdom

1:17pm GMT

Closing Remarks
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Virtual Room C London, United Kingdom

1:17pm GMT

Closing Remarks
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Virtual Room D London, United Kingdom

1:17pm GMT

Closing Remarks
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Wednesday February 19, 2025 1:17pm - 1:20pm GMT
Virtual Room E London, United Kingdom

1:58pm GMT

Opening Remarks
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Virtual Room A London, United Kingdom

1:58pm GMT

Opening Remarks
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Virtual Room B London, United Kingdom

1:58pm GMT

Opening Remarks
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Virtual Room C London, United Kingdom

1:58pm GMT

Opening Remarks
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Wednesday February 19, 2025 1:58pm - 2:00pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Algorithmic and Information Support in Atmospheric Air Quality Monitoring Systems
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Bella Holub, Viktor Kyrychenko, Dmytro Nikolaienko, Maryna Lendiel, Dmytro Shevchenko, Andrii Khomenko
Abstract - The article discusses the informational and algorithmic support for an atmospheric air quality monitoring system. It describes the system's architecture and individual components, along with a logical data model and two approaches to calculating the air quality index. Research on the use of caching methods, pre-aggregation, and sorting is presented to improve the efficiency of processing large volumes of data (Big Data).
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Classifying Customer Feedback using Machine Learning: A Case Study on the Smartphone Supplier’s VOC Dataset
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Nguyen Ngoc Tu, Phan Duy Hung, Vu Thu Diep
Abstract - In today's Industry 4.0 era, information technology has penetrated every industry, making work easier, faster and helping businesses operate more effectively. The ultimate measure of a business's success is customer satisfaction and loyalty. This work aims to enhance customer care by automating the processing of customer feedback through the development of an automatic classification system using deep learning techniques, specifically the Long Short-Term Memory model. The system will automatically classify customer problems, thereby improving service quality and enhancing the company's image. The study used customer feedback data from our company's customer care system, including 41,886 comments from Vietnamese customers. The study proposes to use the LSTM model to process text data and solve the problem of imbalanced data to improve the accuracy and efficiency of the classification system. Test results of the models show that the highest accuracy is about 80%. The study also recommends improving data labeling and testing more advanced natural language processing techniques to achieve better performance in the future.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Priority Classification System of Test Cases for Software Businesses
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Pham Hong Duong, Phan Duy Hung, Vu Thu Diep
Abstract - Text classification, is a very popular problem with various application in natural language processing (NLP). One of the core tasks performed in text classification is assigning labels or tags to units in the text data such as sentences, paragraphs, and documents by exploring the relation between words or even characters. There are many applications derive from text classification, namely Sentiment Analysis, Topic Classification, Spam Detection, Document Classification, and so on. The main object of analyzing is text data. It can come from various sources like a newspaper, a document, some text messages that people use on daily basis. Naturally, as one of the most important form of communication, text is an extremely rich source of data. However, due to its unstructured nature and highly dependence on the context of use, extracting insights from text can be very challenging and time-consuming. This study focuses on exploring the data and forming a classification model on some of the gaming application test sets. We approach the problem using some basic text analysis methods and performing text classification by applying a Deep Learning method – the Convolutional Neural Network model. The dataset is collected from the handwritten test sets for various in-game content by the Quality Assurance Engineers. The main label to be classified is the Priority of the test cases on a whole test set, and eventually, the priority will be used to choose which test case fall into the Regression Test set, specifically 4 types of Priority from highest to lowest label. Finally, we provide an analysis of the performance of deep learning models based on the evaluation metrics as well as comparing it with a self-built traditional Machine Learning model using Logistic Regression and testing against real test case input. From that, we expect to learn to improve the deep learning model and discuss the possible future directions.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Quantum Encryption for low-orbit vehicles
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Makhabbat Bakyt, Khuralay Moldamurat, Luigi La Spada, Sabyrzhan Atanov, Zhanserik Kadirbek, Farabi Yermekov
Abstract - This paper presents a geographic information system for monitoring and forecasting the spread of forest fires based on intelligent processing of aerospace data from low-orbit vehicles (LOA). The system uses convolutional neural networks (CNN) for fire detection and recurrent neural networks (RNN) for fire spread forecasting. To ensure the security of high-speed data transmission from LOA, a quantum key distribution (QKD) system is implemented, providing virtually unbreakable encryption. Experimental results demonstrate a 30% improvement in fire detection efficiency compared to traditional methods. The paper also discusses the potential costs of implementing QKD and AI, as well as the steps required for practical implementation of QKD on a large scale, taking into account factors such as the influence of the atmosphere on quantum key distribution.
Paper Presenters
avatar for Makhabbat Bakyt

Makhabbat Bakyt

Kazakhstan
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Robust Control Strategies for Emergency Situations
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Hiep. L. Thi
Abstract - This paper investigates robust control strategies for managing unmanned aerial vehicles (UAVs) and other systems in emergency situations. We explore the challenges associated with maintaining stability and performance under unforeseen and critical conditions, present current approaches to robust control, and propose new methodologies to enhance system resilience. The paper also discusses practical applications and future research directions in this vital area of control systems engineering.
Paper Presenters
avatar for Hiep. L. Thi
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Towards Sustainable Agricultural Development in Developing Countries through Advanced Frugal Innovations: A Scoping Review and Research Agenda
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Fisiwe Hlophe, Sara Saartjie Grobbelaar
Abstract - By adhering to a systematic design approach informed by scientific and engineering principles, Advanced Frugal Innovations yield products that optimize resource utilization, enhancing environmental sustainability and achieving significant cost savings. Following the Joanna Briggs Institute (JBI) framework, this article presents a scoping review that explores the landscape of AFIs in agriculture in developing countries. The Bibliometrix software package was used to facilitate the analysis of the bibliometric data included in this study. This study discovered that AFIs are based on advanced engineering techniques facilitated by research and development and rigorous design. This allows them to be suitable for mass production and have a wide range of novelty. The significant cost savings allow AFIs to be competitive in all markets, not exclusive to lower-income markets. This study discovered that factors such as a suitable innovation ecosystem, user-centered design, availability of highly skilled labor, and technology development enable the generation and development of AFIs. In contrast, skills shortage, lack of cohesion, funding issues, regulatory issues, and market access are some of the hindrances to the development of AFIs. We propose a research agenda for a better understanding of the requirements for setting up innovation ecosystems in the agricultural context that will drive the development and wide adoption of AFIs.
Paper Presenters
avatar for Fisiwe Hlophe

Fisiwe Hlophe

South Africa
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Computing political power: The case of the Spanish parliament
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Aitor Godoy, Ismael Rodriguez, Fernando Rubio
Abstract - In this paper we present a series of algorithms to calculate the power of each political party in a parliamentary system. For this purpose, it is necessary to calculate the proportion of parliamentary majorities in which their participation is necessary. The usefulness of the proposed methods is illustrated with a real case study: the Spanish electoral system. For this system, we analyze all the elections that have taken place since the establishment of democracy in the country. For each electoral process, we compare the power that each party would have if the allocation of deputies were proportional to the number of votes, and the real power it has with the current electoral system. The results obtained contradict intuitions that the Spanish population usually has about its own electoral system.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Conceptual Framework for Intelligent Road Safety Assessment for Designers (IRSA4D)
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Edura Halim, Azman Mohamed, Mohamad Syazli Fathi, Zeeshan Aziz
Abstract - Roads and highways largely contributes to the growth and development of a country. This paper focuses on enhancing present road safety assessment tools and techniques in line with Safe Roads, one of the pillars of the Safe System. Road Infrastructure Safety Management (RISM) is a comprehensive approach to road safety that involves a set of procedures with systematic identification, assessment, and management of risks associated with road infrastructure. This paper presents a SWOT analysis of tools used in RISM during the design stage including Road Safety Audit (RSA), Road Safety Impact Assessment (RSIA), Road Safety Screening and Appraisal Tools (RSSAT), Star Rating for Designer (SR4D) and Safe System Assessment (SSA). Conceptual framework of Intelligent Road Safety Assessment for Designer (IRSA4D) has been developed to utilized and address the findings on the SWOT analysis of RISM tools. IRSA4D application is aimed to facilitate road designers in providing proactive safety assessment and recommendations for improvement through intelligent static and dynamic assessment. The application is deemed to be valuable in assisting road designers in spite of their level of knowledge and working experiences as well as providing aid in producing the optimum and ‘best’ design during design stage.
Paper Presenters
avatar for Edura Halim

Edura Halim

Malaysia
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Continuous Wavelet Transform based across scale-functional connectivity matrix for motor Imagery EEG classification utilizing Modified EEG Morlet and LSTM Deep Neural Network
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Balendra, Neeraj Sharma, Shiru Sharma
Abstract - Brain-Computer Interface (BCI) technology stands at the forefront of interdisciplinary research, merging neuroscience, engineering, and computer science to forge direct communication channels between the human brain and external devices. BCI based devices has tremendous applications in prosthetic device development. The challenges in real-time practical BCI implementation are due to the bulky models, inherent noises, artifacts and complexity of motor imagery (MI) electroencephalogram (EEG) data with inter-subject and intra-subject variabilities. To overcome these challenges, the proposed algorithm introduces a modified EEG Morlet (MEM) wavelet having a better time bandwidth product leading to detailed feature extraction with capability of natural filter for artifacts and noises introduced by eye blinking and muscle movements. Further, the proposed approach utilizes Hilbert transform to extract temporal features of analytical signal, extract their common spatial patterns, calculates the continuous wavelet transform (CWT) coefficients, arrange these coefficients at different scale for each channel, calculates the cross-correlation for each scale and observes the evolution in cross-correlation matrices at different scale with the help of customized long-short term memory (LSTM) neural network to classify MI EEG. The customized LSTM architecture had the size of 1.93MB showing the effectiveness of methodology for MI EEG classification of embedded based devices. The best classification accuracy achieved by MEM wavelet with instantaneous magnitude temporal feature was 83.78% and the comparative analysis with earlier state of the art methods showed an improvement of 1.10% in accuracy.
Paper Presenters
avatar for Balendra
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Personality Traits in Crowd-based Requirements Engineering
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Ahmed D. Alharthi
Abstract - Traditional proficiency and qualification criteria in crowd-based requirements engineering (CrowdRE) often fall short when identifying the most capable individuals for critical tasks such as elicitation and analysis. While crowd profiles typically account for demographic information like gender and nationality, they frequently overlook personality traits, which can significantly influence task performance. This study addresses this limitation by examining the relationship between personality traits and key requirements engineering (RE) activities within CrowdRE environments. We propose an automated system that incorporates personality profiling into task assignment processes, enhancing the precision of matching individuals to specific RE tasks. By employing data fusion techniques and decision-making algorithms, the system improves the efficiency and effectiveness of task allocation. An empirical investigation is conducted to highlight the impact of personality traits on the success of RE tasks and the need to incorporate these factors into task assignment strategies. The findings contribute to developing intelligent, human-centred collaboration technologies that optimise workflows in crowd-sourced environments. This research underscores the importance of personality traits in improving task performance and collaboration within large-scale ICT systems, aligning with the broader objective of enhancing task allocation in the context of modern collaboration technologies.
Paper Presenters
avatar for Ahmed D. Alharthi

Ahmed D. Alharthi

Saudi Arabia
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Smart Document Management: Harnessing Azure OpenAI’s Generative AI Chatbots to Boost Enterprise Efficiency
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Youssef Baklouti, Tarik Echcherqaoui, Ines Abdeljaoued-Tej
Abstract - This release introduces an innovative interactive chatbot designed to engage with sensitive enterprise data. Leveraging Azure Machine Learning Promptflow and Retrieval-Augmented Generation (RAG) architecture, the chatbot facilitates secure data retrieval and generation within the enterprise environment. To assess the model’s performance, we utilized over 10 query examples, providing ground-truth and context data. Evaluation strategies included system-based metrics like the F1-Score, which yielded an average score of 0.59, and AI-evaluating- AI metrics such as Coherence, Groundedness, Similarity, Fluency, and Relevance, scoring 4.50, 4.20, 4.50, 4.10, and 4.40 respectively. While AI-evaluating-AI strategies showed decent scores, the relatively low F1- Score indicates potential for improvement through fine-tuning or selecting a more suitable vector database. Overall, this interactive solution not only enhances internal operations but also demonstrates AI’s potential in automating and streamlining complex processes.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

The Family Positive Parenting Movement: The Solution to the Root of the Nation's Problems in the Industrial Era 4.0 in Indonesia
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Azam Syukur Rahmatullah, Nurul Fithriyah Awaliatul Laili, Akbar Nur Aziz
Abstract - This study explores the root of social pathologies in the industrial era 4.0, which young and old adolescents in Indonesia carry out. Social pathological behavior that is carried out is quite dangerous to the existence of the nation and state if not handled early. This type of research is qualitative research that is directly studied in the field to find out what causes social pathologies in this industrial era 4.0 and how to solve them. The research approach is phenomenology because it wants to thoroughly explore three (3) informants, experts in the parenting field, taken from the Yogyakarta area of Indonesia, who were interviewed directly by the researcher. The data obtained was then analyzed in depth with phenomenological analysis. The study results show several anomalous social behaviors in the industry 4.0 era caused by parenting in families that are not positive. Hence, they continue until adolescence, adulthood, and even old age. Inconsistency in parenting and failure in parenting cause children to grow into unhealthy individuals and have sick souls so that on their way, they become people who deviate from their behavior. Therefore, healthy and positive parenting must be encouraged early, including in rural and urban areas. Some positive parenting movement programs that should be implemented early on include creating a healthy parenting movement nationally, creating village and city parenting homes nationally, and making instructors and families healthy home companions nationally.
Paper Presenters
avatar for Akbar Nur Aziz
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

An Early Warning System Model for Chicken House Environment and Disease Detection Based on Machine Learning
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Given Sichilima, Jackson Phiri
Abstract - the health and productivity of poultry farms are significantly impacted by the timely detection of diseases within chicken houses. Manual disease monitoring in poultry is laborious and prone to errors, underscoring the need for sustainable, efficient, reliable, and cost-effective farming practices. The adoption of advanced technologies, such as artificial intelligence (AI), is essential to address this need. Smart farming solutions, particularly machine learning, have proven to be effective predictive analytical tools for large volumes of data, finding applications in various domains including medicine, finance, and sports, and now increasingly in agriculture. Poultry diseases, including coccidiosis, can significantly impact chicken productivity if not identified early. Machine learning and deep learning algorithms can facilitate earlier detection of these diseases. This study introduces a framework that employs a Convolutional Neural Network (CNN) to classify poultry diseases by examining fecal images to distinguish between healthy and unhealthy samples. Unhealthy fecal images may indicate the presence of disease. An image classification dataset was utilized to train the model, which achieved an accuracy of 84.99% on the training set and 90.05% on the testing set. The evaluation indicated that this model was the most effective for classifying chicken diseases. This research underscores the benefits of automated disease detection within smart farming practices in Zambia.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Droop Control Optimization Based on Gray Wolf Optimizer for AC-Microgrid
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Ruqaya Majeed Kareem, Mohammed Kh. Al-Nussairi
Abstract - Since the establishment of microgrids, the frequency stability and reliability in operating the voltage of microgrids have become necessary due to local sources of reactive power. Droop control technology has been successfully applied to this problem and remains popular today. This study proposes a control strategy that can be utilized to power-sharing and adjust the voltage and frequency appropriately according to the load condition. The main aim of the research is to control the frequency and voltage of microgrids under various conditions by using two algorithms, the Gray Wolf Optimizer (GWO) and Kepler Optimization Algorithm (KOA) to optimize the droop control and optimize the PI controller parameters. Simulation findings using Simulink in MATLAB demonstrate the performance of the suggested microgrid stability techniques. Finally, to evaluate the efficiency of the suggested control strategy, its results are compared with conventional methods.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Enhancing IoT Security and Malware Detection Based on Machine Learning
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Ahmed Abu-Khadrah, Munirah Ali ALMutairi, Mohammad R. Hassan, Ali Mohd Ali
Abstract - The Internet of Things (IoT) devices are employed in various industries, including health care, smart homes, smart grids, and smart cities. Researchers address the intricate connection between the growth of the Internet of Things and the hazards to its security. The vast and varied features of the Internet of Things make traditional security solutions ineffective. A new model is developed to enhance IoT malware detection by combining three machine learning algorithms: KNN, Bagging, and support vector machines. The proposed model is evaluated by measuring accuracy, precision, recall and F1-score. In addition, two comprehensive datasets are utilized to evaluate the proposed model dataset. The study explores the potential of three ensemble classification models for Malware Detection. This study investigated the efficacy of a novel ensemble machine-learning approach for detecting malware within the Internet of Things (IoT) domain. The result of this research is that the accuracy on the validation set is 95.76%, the precision on the validation set is 97.01%, the recall is 94.55%, and the F1 score is 95.77%. The findings of this study indicate that the proposed model, a synergistic combination of K-Nearest Neighbours (KNN), Bagging, and Support Vector Machines (SVM), achieved a commendable overall accuracy of 95.76% in correctly classifying both malware and benign programs within the utilized IoT dataset.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Fake Beef Detection Using Lightweight Convolutional Neural Networks
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Hoang Minh Tuan, Ngo Gia Quoc, Nguyen Huu Tien, Vu Thu Diep
Abstract - This paper provides a method for automatically detecting fake beef by image analysis. High-quality classification models could have a major impact on ensuring food quality, supporting supply chain management in the meat industry, and preventing fraudulent commercial practices. Because low-quality meat is cheaper and more widely available than beef, it is common to use them as a substitute for fake beef. The problem is due to the differences in meat appearance, texture, mutilation, and color of cuts, as well as similarities between real beef and fake beef. These characteristics require a robust method to distinguish subtle characteristics to obtain reliable results. This paper combines the strength of Convolutional Neural Networks to detect a true classification of beef and fake beef. This model targets mobile applications and is suitable for the practical deployment of various environments.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

System Integrative Framework for Evaluating the Effectiveness of KNUST Enterprise System: A Case Study of a Ghanaian University
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - George Kwamina Aggrey, Amevi Acakpovi, Emmanuel Peters
Abstract - ERP systems are integrated information systems (IIS) popularly used among tertiary institutions in the globe. ERP has attained familiarization in certain parts of the globe due to its huge acquisition in tertiary institutions. Notwithstanding the rising acquisition, choice and execution of ERPs in higher education, there remains a scarcity in literature about their performance especially in the developing world. It is, therefore, important to further examine whether these ERPs fulfill their anticipated benefits. This paper aims to evaluate the effectiveness of KNUST's enterprise system (comprising ARMIS, Panacea, and Synergy Systems) in HEIs through a system integrative framework. A combined-method research approach was employed, collecting data from a sample of 60 respondents for both quantitative and qualitative investigation. The data were examined through partial_least_squares structural_equation_modeling (PLSSEM) and inductive_thematic_analysis. The study's results revealed that the customer/stakeholder-perspective, learning-growth-perspective, financial-perspective and system-quality-perspective significantly influence and positively relate to the effectiveness of KNUST's enterprise system evaluation in Ghanaian higher education. Internal business process, according to the findings, was the only perspective that had no significant impact on the performance of KNUST enterprise system in the Ghanaian higher education. Works on ERPs assessments, readiness, and implementations are scarce in developing world, particularly in the Ghanaian context. This study has successfully assessed the KNUST enterprise system, demonstrating its effectiveness through the research model deployed.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Understanding ENSO Teleconnections’ Influence on Drought in Southern Africa: A Machine Learning Approach
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Jimmy Katambo, Gloria Iyawa, Lars Ribbe, Victor Kongo
Abstract - The vulnerability of Southern Africa to climate variability, especially drought, places substantial pressure on agriculture, water systems, and the economy. This study explores how El Niño-Southern Oscillation (ENSO)-related Sea Surface Temperature (SST) variations influence drought patterns across the region using machine learning methods. Two approaches were taken: (i) a feature ranking of SST in comparison to twelve other climate variables and (ii) drought model performance comparisons with and without SST data. Results reveal SST’s significant and consistent impact across all climate zones, with both methods indicating that SST data, particularly in connection with ENSO phases, strongly influences drought variability, despite slight variations in its order of effect with respect to climatic zonal divisions. This underscores the value of incorporating SST in climate models for enhanced drought prediction and adaptation planning. Although limited by a focus on SST and not fully accounting for interactions with other climate factors, this research provides a solid foundation for understanding regional climate dynamics. Adding more climate indicators and studying SST’s interactions with land-based factors could help future studies make drought predictions more reliable and better prepare vulnerable areas.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Continuous Learning System for Detecting Anomalies in Daily Routines Using an Autoencoder
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Dominic Gibietz, Daniel Helmer, Eicke Godehardt, Heiko Hinkelmann, Thomas Hollstein
Abstract - The ongoing demographic change towards an aging population increases the need for effective solutions to support independent living and ensure the safety of elderly people living alone. Detecting anomalies in the daily routines of these people is a critical task in order to address these challenges and maintain their well-being. This paper proposes an unobtrusive method for anomaly detection using binary sensor data and machine learning. The approach involves a neural network in form of an autoencoder, which evaluates hourly data of each room, including the accumulated residence time, the activity time, and the number of room entries. The system learns individual normal behaviour through online learning and detects deviations from it. Testing and evaluation of the system was carried out using a publicly available dataset and comparing different configurations for the model. A comparison was also made between the use of individual maximum values for each room to normalize the data and uniform values for all rooms, with the former performing significantly better. The results demonstrate that the system can effectively identify the majority of unusual daily routines with a high accuracy, offering potential for improving safety measures for people living alone.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Contract Pre-Review Assistance System Based on RAG and LLM
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Chi-Hung Wang, Xiang-Shun Yang, Jun-Yi Liu, Yao-Jun Liu
Abstract - Contract review is a common challenge for governments, businesses, and individuals. It becomes challenging when manual reviews are slow, legal expertise is lacking, and clauses are complex. These issues often lead to legal disputes and business conflicts. Traditional rule-based contract review tools often struggle with ambiguous language and unstructured content. Large language models (LLM) can quickly analyze contracts and find risks. But, they are unreliable due to "hallucinations" and a lack of knowledge of rare clauses. This study used retrieval-augmented generation (RAG) technology to overcome these challenges. It integrated verified legal data with large language models. This improved review accuracy to 93.67%. The F1-scores reached 91.95% for compliant clauses and 94.79% for non-compliant ones. The ROC-AUC metric improved to 0.93. The results show that this approach works. It improves the classification and risk identification of contract clauses. It also helps in contract review in the legal and business sectors, promoting the use of legal tech.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Data Science Implementation For Social Empowerment
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Samrat Ray, Souvik Datta, Smita Mehendale, Mita Mehta
Abstract - The use of big data in social justice has become a phenomenon that is transforming the entire society, given that it provides solutions to challenges facing the world through the betterment of the lives of the affected groups of people. This paper focuses on the role of positive change by means of data science with a special emphasis on real-time data analysis in supporting power to the people efforts. It starts with the introduction of Data Science approaches and their connection with social transformation focusing on how it has made it possible for organizations to make sound decisions followed by the practical use of real-time big data to support research claims through the use of real-life case scenarios including poverty alleviation, city planning and development among others. Insights from these shed the light on ethical issues and need to make a conscious effort towards making data science solutions available for every segment of society. Finally, the paper analyses the trends and the future possibilities of data science for social enablement. It highlights the possibility of achieving even greater improvement of social programs through advanced research and development. In conclusion, this paper is a summary of how data science can be used to make society better which should prove useful as a reference for policymakers, researchers, and practitioners who are using data to initiate social change.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

DE-OVDR: Depth Estimation and Open Vocabulary Detection for Object Removal
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Chi-Hung Wang, Yu-Siang Siang, Yu-Hsuan Lin, Cheng-Hsien Lin
Abstract - Aerial imagery is widely employed in intelligent transportation management and urban planning. However, dynamic objects often occlude critical information such as road signs and traffic markings, reducing the accuracy of image analysis and thereby affecting application reliability. Although traditional methods can partially address this issue, their high cost and low efficiency pose challenges in large-scale data processing. To overcome these limitations, this study proposes a background averaging technique based on real-time open-vocabulary object detection integrated with difference-based object detection using depth estimation. This approach enables zero-shot dynamic object removal, enhancing both processing efficiency and scalability. Experimental results demonstrate that our technique outperforms conventional methods across multiple performance metrics. Specifically, the multimodal framework combining depth-based differencing with the YOLO-world model achieves Precision, Recall, and F1-Score of 0.9062, 1.0000, and 0.9508, respectively. Furthermore, the Structural Similarity Index (SSIM) for background reconstruction reaches 0.9603, exceeding that of traditional YOLO models (SSIM = 0.9375). These findings indicate that our method not only effectively removes dynamic objects but also accurately restores background information, providing robust support for applications in intelligent transportation management and urban planning.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Development of Hybrid AI Model-Assisted Bilingual Chatbot for Stunting Education and Nutrition Status Classification
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Wa Ode Siti Nur Alam, Riri Fitri Sari
Abstract - The rapid development of artificial intelligence has facilitated the creation of Chatbot AI systems capable of addressing diverse healthcare challenges, including public education on critical issues like stunting. Leveraging Generative Pre-Trained Transformer (GPT) models and ensemble learning methods, such systems provide accurate, bilingual responses while ensuring scalability. A key implementation, deploying a Bilingual Chatbot AI through the Telegram application, demonstrates the feasibility of using accessible platforms to disseminate vital healthcare information. However, AI chatbots often face limitations, such as inaccurate or delayed responses, hindering user satisfaction and trust. Challenges in stunting education and nutritional status classification include adapting to linguistic nuances and ensuring real-time interaction. Addressing these gaps, we developed a GPT-Ensemble Learning-based chatbot to deliver information about stunting, including its definition, symptoms, impacts, prevention measures, and classification of toddlers' nutritional status based on gender, age, and height. The chatbots provide relevant responses for stunting education and nutritional status classification in Indonesian and English contexts. Our experiments also highlight Random Forest as the optimal ensemble model, achieving exceptional performance metrics: accuracy (0.99), precision (0.99), recall 0.96, F1-score (0.99), and ROC-AUC (0.99). This high performance ensures reliable nutritional status classification while improving accuracy and speed in bilingual interactions. The results underscore the potential of integrating AI-driven solutions into accessible applications like Telegram, which has significant implications for improving public health awareness and decision-making.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

SF-AE: Split Federated Autoencoder for Unsupervised IoT Intrusion Detection
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Authors - Andrea Augello, Alessandra De Paola, Domenico Giosue, Giuseppe Lo Re
Abstract - Smart systems have become increasingly popular in recent years, widening the attack surface of cyber threats. Machine learning algorithms have been successfully integrated into modern security mechanisms to detect such attacks. Internet of Things (IoT) systems often have limited computational resources and are unable to execute entire machine learning pipelines. However, these systems often produce and manage sensitive data. Thus, it is preferable to avoid exposing their data to external analysis, e.g., on cloud systems. This work introduces SF-AE: a novel architecture that enables the distributed training of an anomaly-based intrusion detection system on devices with limited computational resources without exposing sensitive data. Experimental results on multiple datasets show that SF-AE outperforms state-of-the-art methods in terms of attack detection performance, at lower computation and communication costs for the participating devices.
Paper Presenters
Wednesday February 19, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

3:30pm GMT

Session Chair Remarks
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Virtual Room A London, United Kingdom

3:30pm GMT

Session Chair Remarks
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Virtual Room B London, United Kingdom

3:30pm GMT

Session Chair Remarks
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Virtual Room C London, United Kingdom

3:30pm GMT

Session Chair Remarks
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Wednesday February 19, 2025 3:30pm - 3:33pm GMT
Virtual Room D London, United Kingdom

3:33pm GMT

Closing Remarks
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Virtual Room A London, United Kingdom

3:33pm GMT

Closing Remarks
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Virtual Room B London, United Kingdom

3:33pm GMT

Closing Remarks
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Virtual Room C London, United Kingdom

3:33pm GMT

Closing Remarks
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Wednesday February 19, 2025 3:33pm - 3:35pm GMT
Virtual Room D London, United Kingdom

4:13pm GMT

Opening Remarks
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Virtual Room A London, United Kingdom

4:13pm GMT

Opening Remarks
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Virtual Room B London, United Kingdom

4:13pm GMT

Opening Remarks
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Virtual Room C London, United Kingdom

4:13pm GMT

Opening Remarks
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Wednesday February 19, 2025 4:13pm - 4:15pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Accessibility Barriers in Complex Data Tables
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Karen McCall, Bevi Chagnon
Abstract - The advent of the Internet and digital content has underscored the need to ensure equal access to data tables for individuals with disabilities, particularly those who are blind. However, the conventional 'one size fits all’ solutions, akin to Alt Text, have proven inadequate in the face of the growing volume of complex digital data tables. This paper presents research findings that could significantly enhance the accessibility of complex data tables for all users. Past and current research typically focuses on two areas of digital table access: HTML and PDF, and simple rather than complex data tables [1] [2] [3] [4]. For those using screen readers, basic information about a data table is provided with two options. It is either a “uniform” or simple data table or a “non-uniform” complex data table, which can have potential accessibility barriers such as merged or split cells. This paper provides insight and the results of original research in the form of an end-user survey on the multifaceted accessibility barriers of complex data tables. The research highlights that any solution designed for those using screen readers can benefit everyone — regardless of their abilities — in understanding complex data tables. This inclusive approach not only underscores the value of our work to all users, making them feel included and valued, but also holds the promise of a more accessible digital future across all digital technologies and media formats and for all users.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Analysis of the use of LKT for the teaching of Physical Education in the General Unified High School
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Luz Norma Caisal, Mocha-Bonilla Julio A.
Abstract - The so-called digital era in which we live together with Learning and Knowledge Technologies (LKT) have radically transformed the form and methods of teaching and learning, LKT are tools that have evolved digital teaching towards the creation of learning experiences personalized and meaningful. One of the application contexts focuses on the teaching of Physical Education, an area that presents a wide variety of strategies in the teaching-learning process, therefore, Physical Education is an area where various technological tools can be incorporated for teaching. and practice of Physical Education. We worked with a group of students belonging to the third year of the Unified General Baccalaureate, the sample was made up of 84 students, who are aged ±16 years, as an instrument a structured questionnaire with polytomous questions distributed in three sections was used, the processing and Data analysis was carried out using the IBM SPSS Statistics version 24 package. The results in the first section reflect that students feel satisfied or very satisfied when practicing physical education; In the second section, it could be assumed that 89% of the students, the vast majority, used, applied and improved their learning thanks to learning and knowledge technologies in the physical education teaching process; Finally, in the third section, the use of the most used technological tools such as Genially, Google Meet, Kahoot, Moodle platform, Prezi and Socrative is emphasized. It is concluded that in physical education the application of Kinovea in physical education processes is essential to improve movement human.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Dataset generating methods for best facial expressions classification in machine learning
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Robert, Tubagus Maulana Kusuma, Hustinawati, Sariffudin Madenda
Abstract - The process of forming a good dataset is a very decisive step in the success of a facial expression recognition/classification system. This paper proposes 24 scenarios for the formation of facial expression datasets involving the Viola-Jones face detection algorithm, YCbCr and HSV color space conversion, Local Binary Pattern (LBP), and Local Monotonic pattern (LMP) feature extraction algorithms. The results of the 24 dataset scenarios were then formed into five dataset categories to be used as training datasets and testing of two Machine Learning calcification models, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The SVM classification model is designed using four different kernels: radial, linear, sigmoid, and polynomial basis functions. Meanwhile, the CNN classification model uses the MobileNetV2 architecture. From testing the five categories, the best accuracy result is 83.04% provided by the SVM classifier that uses the sigmoid kernel and a combined dataset of LBP and LMP features extracted to focus only on the facial area from the results of the Viola- Jones face detection algorithm. In addition, for the CNN classifier, the best accuracy was obtained at 82.14% by using the Y-grayscale dataset which also focuses only on the facial area but without the feature extraction process. The results of the best accuracy for the two classifiers show that the face detection stage plays an important role in the facial expression recognition/classification system. The LBP and LMP algorithms are good enough to use for feature extraction in forming datasets in the SVM classification model.
Paper Presenters
avatar for Robert

Robert

Indonesia
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Syntax-Constraint-Aware SCABERT: Syntactic Knowledge as a Ground Truth Supervisor of Attention Mechanism via Augmented Lagrange Multipliers
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Toufik Mechouma, Ismail Biskri, Serge Robert
Abstract - This paper introduces Syntax-Constraint-Aware BERT, a novel variant of BERT designed to inject syntactic knowledge into the attention mechanism using augmented Lagrange multipliers. The model employs syntactic dependencies as a form of ground truth to supervise the learning process of word representation, thereby ensuring that syntactic structure exerts an influence on the model’s word representations. The application of augmented Lagrangian optimisation enables the imposition of constraints on the attention mechanism, thereby facilitating the learning of syntactic relationships. This approach involves the augmentation of the standard BERT architecture through the modification of the prediction layer. The objective is to predict an adjacency matrix that encodes words’ syntactic relationships in place of the masked tokens. The results of our experiments demonstrate that the injection of syntactic knowledge leads to improved performance in comparison to BERT in terms of training time and also on AG News text classification as a downstream task. By combining the flexibility of deep learning with structured linguistic knowledge, we introduce a merge between bottomup and top-down approaches. Furthermore, Syntax-Constraint-Aware BERT enhances the interpretability and performance of Transformerbased models.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Transfer and Application of Artificial Intelligence Technology in Digital Marketing Strategy of Latvian Companies
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Anda Batraga, Tatjana Volkova, Jelena Salkovska, Liene Kaibe, Didzis Rutitis, Eduards Aksjonenko, Marta Kontina
Abstract - As AI develops, it is becoming increasingly important in digital marketing processes. AI has become an essential part of the digital marketing world, enabling businesses to reach their customers faster and to improve their business operations by automating some of the simplest tasks. Through technology transfer, AI brings significant improvements offering new opportunities and creative approaches to achieving the goals of a digital marketing strategy. The aim of this study is to investigate and analyse the possibilities of using AI in digital marketing strategy in order to draw conclusions and make proposals on the possibilities of improving digital marketing strategy in Latvian companies using AI. The results show that the transfer of AI technology can provide companies with several advantages. The need for a well-thought-out technology transfer is emphasised by the experts in order to make the technology work and help the company achieve its goals.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

VPNs (Virtual Private Networks) for Securing Public Wi-Fi
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Lerato Mashiloane, Khutso Lebea
Abstract - Since the beginning of the internet, there has been a continuous effort to secure and encrypt data transmitted through web browsers. The acronym VPN stands for "Virtual Private Network", which refers to the capability of creating a secure network connection while using public networks. Commercial and defence organisations have also adopted virtual private networks because they offer secure connectivity at reduced costs. The research paper will discuss what VPNs are, their importance and the mechanics behind them to give users an understanding of their highest level of security. The paper will look further at factors to consider when choosing a VPN and the balance between security and performance.
Paper Presenters
avatar for Khutso Lebea

Khutso Lebea

South Africa
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

ANFIS-PDLC Based Real-Time Solar Radiation Controller for photovoltaic systems
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Nabaa T. Salman, Wasan A. Wali, Mohammed Lami
Abstract - Solar energy is an inexhaustible source of carbon-free energy world-wide. However solar radiation determines the amount of electrical energy and current that can be produced by solar panels. In this paper, the dynamic regulation of solar radiation using Polymer Dispersed Liquid Crystal (PDLC) was studied. It regulates solar radiation that is incident on the solar cell’s surface by changing its transparency because of the applied voltage. Thus, researchers were able to obtain a variable range of solar radiation at the same time and then control the power of the solar cell produced according to the user's request. In the outdoor experiment, the behavior of filtrated radiation in daylight performance under different sky circumstances was assessed by examining solar radiation both with and without a PDLC screen. To simulate the PDLC and forecast solar radiation in real time, the researchers utilized an Adaptive Neuro-Fuzzy Inference System (ANFIS). MATLAB program utilized 3.5 W, 25 W, and 100W solar panels. PDLC transparency ranges from 5% to 83%. The results showed that PDLC overall shading on transparent\opaque states are 73% to 37% respectively at the same point where PDLC films may regulate the solar cell's output power at a pace of (39.6%, 39.5%, 42%) of the total cell power for the simulated solar panel respectively.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Artificial Intelligence Model for Predicting Weather Conditions at an Airport
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Miguel Angel Ruiz-Adarmes
Abstract - Knowing the weather conditions at airports is of vital importance for reasons that affect the safety, efficiency, and comfort of flights. Bad weather, such as strong winds or fog, can represent a significant danger to aircraft during takeoffs, landings, and flights. Therefore, learning to predict weather behavior, based on prior information, is important. That is why this research on the prediction of weather conditions at Jorge Ch´avez Airport in Lima is presented. To do this, a set of previous data was used to which the J48, Random Forest, SVM, Bayes Net, and Neural Network algorithms were applied to identify that the Random Forest algorithm obtained the best behavior with Accuracy = 76.8004% for the training and validation process; and Accuracy = 78.5181% for the test process. As a proof of concept, a Java application was implemented.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Automating medical screening processes with machine learning: improving data quality and reducing human errors
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Strahil Sokolov, Kaloyan Varlyakov, Dimitar Radev
Abstract - In this paper an approach is described for improving the quality of data generated from medical screening processes based on machine learning. The designed workflow uses data acquisition from Titmus equipment, performs data preprocessing, model training and health record evaluation. We are proposing a design of a distributed system to realize this approach in order to bridge the gap between the cloud-native technologies and their usage for patient screening in rural or remote areas. The algorithm shows promising results and is suitable for implementation on Edge-AI , IoT and cloud-based medical support systems.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Digitally Gamified Instructional Design (DGID): Insights into Empowering Digital Learning Environment
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

HHO-Enhanced Deep Learning Approach for Accurate Papilledema Detection
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Marwa Mostafa Yassin, Nahla A. Belal, Aliaa Youssif
Abstract - Papilledema is a medical disorder marked by the enlargement of the optic disc. Optic disk imaging is essential for the diagnosis of papilledema, as neglecting to perform this procedure can lead to fatal outcomes. This research presents a novel approach that combines deep learning with the Harris Hawks Optimization (HHO) algorithm to increase the accuracy of diagnosing and distinguishing papilledema in optical disk images. The proposed technique presented in this study focuses on optimizing the weights of the Convolutional Neural Network (CNN) model. This optimization process improves model training by using the underlying optimization principles. The technique was evaluated using the Kaggle dataset, which was made available for this purpose. The evaluation results showed that the proposed technique achieved an accuracy of 0.997%, surpassing the performance of existing techniques such as VGG16, DenseNet121, EfficientNetB0, and EfficientNetB3. The proposed model demonstrates that state-of-the-art CNN models, when paired with the HHO algorithm, can reliably diagnose real papilledema, pseudo papilledema, and normal optic discs. This could potentially save lives for patients.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Unveiling Community Policing Challenges in Nigeria using Greenhalgh’s Meta Analysis Approach
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Otuu Obinna Ogbonnia, Joseph Henry Anajemba, Oko-Otu Chukwuemeka N., Deepak Sahoo
Abstract - Scholars have investigated the challenges of community policing (CP) in Nigeria through socio-political, economic, and cultural lenses, with none adopting a method that can reveal these challenges comprehensively. This has led to a gap in recognizing key CP problems, thereby resulting in ineffective solutions from the government, and making government services in this context less accessible and responsive to citizens. This study employed Greenhalgh’s meta-narrative approach to unveil community policing challenges that were previously overlooked in Nigerian context. Drawing from a variety of sources such as scholarly articles (ACM digital library, Science Direct), official documents, and media coverage, this study identified lack of robust technology usage, lack of citizens’ participation, citizens’ unwillingness to share information and lack of trust, accountability and transparency as major community policing challenges in Nigeria. This study contributes to a nuanced understanding of the challenges hindering the successful implementation of CP in Nigeria, highlights the implications of these challenges on the overall security landscape, and offers directions to policymakers and relevant government agencies, providing insights to the design of technological solutions for community policing in Nigeria.
Paper Presenters
avatar for Otuu Obinna Ogbonnia

Otuu Obinna Ogbonnia

United Kingdom
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Automatic Creation of Visualizations with a Multi-Agent LLM Approach
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Ping Luo, Kyle Gauthier, Bo Huang, Wenjun Lin
Abstract - Data visualization is a critical tool for interpreting complex information, yet it often remains inaccessible to those without extensive technical and analytical skills. This study introduces a novel multi-agent system leveraging a large language model (LLM) to democratize the process of creating high-quality visualizations. By automating the stages of planning, coding, and interpretation, the system empowers users with diverse backgrounds to generate accurate and meaningful visual representations of data. Our approach employs multiple specialized agents, each focusing on different aspects of the visualization workflow, thereby enhancing the overall quality through collaborative problem-solving and contextual communication. The iterative refinement phase ensures that the visualizations meet the initial objectives and data characteristics, thus improving accuracy and relevance. This study’s modular design allows for scalability and adaptability to various data types and visualization needs, ensuring the system remains current with emerging tools and frameworks. By lowering the barriers to effective data visualization, our system supports broader data-driven decision-making across various domains, fostering more inclusive and impactful data analysis practices. Validation on two public datasets demonstrates that our multi-agent framework generates visualizations that achieve comparable or superior quality metrics when benchmarked against human expert analysis.
Paper Presenters
avatar for Ping Luo

Ping Luo

Canada
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Chat GPT-Enhanced Instructional Design: Potentials to Empower Learning Resources and Tool
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Gamification and English for Specific Purposes Learning: Integrating Duolingo as an Innovative Tool for B1+ Students
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Nelly Galora-Moya, Paola Ramos-Medina, Elsa Hernandez-Cherrez, Javier Sánchez-Guerrero
Abstract - This study analyzes the necessity and impact of English for Specific Purposes (ESP) courses on B1+ level students, integrating gamification as an innovative approach through the use of the Duolingo platform. Surveys were ad-ministered to students and faculty members from various departments to gather their perceptions on the relevance and feasibility of gamified ESP courses. Additionally, a preliminary diagnostic test was conducted to assess technical vocabulary knowledge in a gamified environment. The results show a general consensus on the importance of ESP courses, highlighting Duolingo as an effective tool for personalizing learning and enhancing linguistic competencies in specific contexts. Significant gaps in students' linguistic skills were also identified, justifying the incorporation of this methodology. The paper proposes a collaborative program between the faculties of the Technical University of Ambato (UTA) and the Language Center, with Duolingo playing a central role in designing a gamified curriculum to bridge the gap between academic English and the specialized skills required in professional settings. This approach not only improves academic performance but also equips graduates with the linguistic skills necessary to compete in the global job market. Gamification, by combining motivation and interactivity, fosters autonomous and collaborative learning. This work contributes to the current debate on the use of technology in ESP teaching, emphasizing gamification as a key strategy for personalizing learning. Future research should assess the longitudinal impact of gamified platforms like Duo-lingo on academic performance and graduates’ career trajectories.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Leveraging Transfer Learning & CNNs for Classification of Breast Cancer via Ultrasound Images
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Reem M. Zemam, Nahla A. Belal, Aliaa Youssif
Abstract - According to the World Health Organization’s statistical data for 2024, breast cancer is the most often diagnosed cancer among women.Between 2020 and 2024, approximately 37,030 new instances of invasive breast cancer were documented in women.Recent advancements in deep learning have shown considerable potential to improve the accuracy of breast cancer diagnosis, ultimately aiding radiologists and clinicians in making more precise decisions.This study presents a strategy that creates a highly dependable ultrasound analysis reading system by comparing the powerful processing capabilties of CNNs with 4 pretrained models (Transfer Leraners). The models employed were the DenseNet 169, ResNet 152, MobileNet V2, and Xception. To assess the effectiveness of the proposed framework, experiments were conducted using established bench- mark datasets (BUSI datasets). The suggested framework has demonstrated superior performance compared to previous deep learning architectures in precisely identifying and categorizing breast cancers in ultrasound images. Upon comparison of the specified deep learning models, DenseNet 169 had the maximum performance with an accuracy of 99.7%. This surpasses the research undertaken in the literature. This research employs advanced deep learning algorithms to enhance breast cancer diagnostic outcomes, decreasing diagnosis time and facilitating early treatment.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Prolonged Sitting and its Risks: Analysis of Pathologies and Technological Solutions
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Eduardo Pinos-Velez, Adriana Martinez-Munoz, Dennys Baez-Sanchez
Abstract - Prolonged sitting is a significant contributor to various health issues, including pressure ulcers, lower back pain, and circulatory disorders. This paper provides an analysis of these pathologies, examining their underlying causes, physiological impacts, and the compounding role of risk factors such as physical inactivity and poor posture. Furthermore, the study evaluates technological solutions designed to mitigate these risks. These include advanced sensor-integrated cushions and alternating pressure systems that facilitate weight redistribution to prevent tissue damage.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

UNDERSTANDING CRYPTOCURRENCY USAGE BEHAVIOUR AMONG GEN Z IN A DEVELOPING ECONOMY: A UTILITY THEORY PERSPECTIVE
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Desmond Kwadjo Kumi, Sheena Lovia Boateng
Abstract - This study uses the utility theory framework to investigate cryptocurrency awareness and usage behavior among Gen Z in Ghana. Data were collected from 700 individuals in the educational sector of Ghana, aged 18-25 years, through purposive and snowball sampling, using structured questionnaires, with 657 usable responses. Data was analyzed using SPSS, AMOS, and Hayes Process Macro. The results revealed that perceived benefits significantly indirectly affect cryptocurrency usage behaviour via cryptocurrency’s perceived value. Perceived risks lessen the influence of perceived benefits on perceived value, whereas personal innovativeness improves this link. The survey further revealed a very high awareness of Bitcoin and other cryptocurrencies, but comparatively lower awareness of the entire blockchain technology. Whereas, awareness, attitudes toward and ownership of cryptocurrencies were higher among males than females, thus showing a gender gap in the awareness and ownership of digital assets. This study is arguably one of the few sources with insights into applying the utility theory to understand cryptocurrency usage behaviour among Gen Z in a developing economy like Ghana. Practitioners and policymakers could therefore, tailor strategies that address awareness and ownership gaps and optimize utility dimensions.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Augmented Reality in Early Childhood Education: The Active Triangle Kids Project
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Juanjo Mena, Juan Miguel Lorite, Antonio Patrocinio-Braz, Adrian Fernandez
Abstract - In recent times, society has been influenced by technological advancements that have facilitated progress and brought corresponding modifications across various fields and environments, both academic and professional. Within this context, emerging technologies such as augmented reality (AR), virtual reality (VR), extended reality (XR), and the Internet of Things (IoT) have gained prominence. These technologies have significantly contributed to the improvement of diverse areas, including education, industry, and medicine, among others. In this regard, Active Triangle Kids was developed as a project based on augmented reality, specifically designed for children aged 3 to 6 years. The project encompassed the design, planning, and training of an optical recognition application, along with the creation of a demo for a video game utilizing augmented reality. As a result, each component of the project was successfully developed independently, ensuring effective training and programming. The project concludes by highlighting its unique aspects, identifying the current limitations of its components, and outlining potential future directions for further development and improvement.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Digital Administration System for Education Institution: Insights from Technology Adoption to Beneficial Contribution
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Experimental Assessment of the Performance of Meta-resonator Based Band-stop Waveguide Filters Fabricated with CNC Milling and Stereolithography Methods
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Sinan Bicer, Abdulrahman Nasser Abass Abdo, Habib Dogan, Abdullah Genc
Abstract - In this work, using SRR meta-resonators, a band-stopping waveguide filter (WGF) in C band (4-7.5 GHz) is designed and fabricated by using both CNC milling and SLA methods, and the effect of the fabrication methods on the filter performance is experimentally evaluated. The filter order for each case is increased from 1 to 7 and meta-resonators are used as many as the number of filter degrees. To determine the performance of the WGFs, some results such as frequency response, center frequency, fractional bandwidth (FBW), and quality factor (Q) values are given comparatively for each filter order. Also, the simulated and measurement results are in good agreement with each other. The measured results show that the performance of the WGFs fabricated by the CNC milling method is partially better than the filter fabricated by the SLA method. This decrease in SLA performance is thought to be due to the production methods. However, The WGF with the SLA method is nearly 50% lighter in weight than that produced with the CNC method. As a result, the SLA fabrication method is experimentally demonstrated to be a good alternative to conventional fabrication methods such as CNC milling.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Hybrid FinBERT-LSTM Deep Learning Framework for Stock Price Prediction: A Sentiment Analysis Approach Using Earnings Call Transcripts
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Biswadeep Sarkar, Abdul Shahid
Abstract - Stock market prediction remains a critical area of research due to its significant economic implications and inherent complexity. With advancements in machine learning, research interest has grown substantially in understanding the impact of textual data on financial forecasting. This study presents a hybrid FinBERT-LSTM model that combines sentiment analysis of quarterly earnings conference calls with traditional price prediction methods. We evaluate our model’s effectiveness against standalone LSTM approaches across six major US stocks from the financial and technology sectors. Experimental results demonstrate that the sentiment-enhanced hybrid model achieves superior predictive accuracy for four of the six studied stocks, as measured by Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Accuracy metrics. Most notably, Citibank and Meta demonstrated substantial improvements when incorporating sentiment analysis, with MSE scores approximately 38 percent lower compared to predictions without sentiment data. Our findings contribute to the growing body of research on textual analysis in financial forecasting, offering practical implications for investment decision-making and aligning with the United Nations Sustainable Development Goal (SDG) 9 – Industry, Innovation, and Infrastructure.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

On the Capacity of Representation of an E-nose Constructed With Two MOX Sensors
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Italo Santos, Jugurta Montalvao, Luiz Miranda
Abstract - The class representation capacity in signal spaces spanned by arrays of metal oxide sensors (e-noses) is studied in this work. It is addressed in one of its simplest configurations, with a commercial MOX sensor running in two different temperature modulations, working as two different sensors. The class representation capacity of such an array is studied in the information theory framework. It is shown that, for steady-state measurements without drift, only a few tens of classes can be properly accommodated in the corresponding signal space, under moderate levels of noise.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Safeguarding Autonomous Transportation: Deep Learning Strategies for Detecting Anomalies in Vehicle Sensor Data
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Authors - Elvin Eziama, Remigius Chidiebere Diovu, Gerald Onwujekwe, Jacob Kapita, Victor L.Y. Jegede, Jegede T.T. Jegede, Solomon G. Olumba, Harrison Edokpolor, Adeleye Olaniyan, Paul A. Orenuga, Anthony C. Ikekwere, Emmanuel A. Ikekwere, Uchechukwu Okonkwo, Egwuatu C.A. Egwuatu, Charles Anyim, Jacob A. Alebiosu, Victor N. Mbogu, Benjamin O. Enobakhare, Toheeb A. Oladimeji, Anthony Junior Odigie, Adeleye Olufemi
Abstract - By improving reliable communication between cellular vehicle-to-everything (CV2X), intelligent transportation systems (ITS) have the potential to revolutionize the real-time transportation sector. However, one element that hinders the seamless deployment of ITS is security issues. Resource limitations, anomaly types, false positives, and sensor interference are among the difficulties. Discrete Wavelet-Based Deep Reinforcement Learning with Double Q Learning (DWT-DDQN), a robust hybrid approach that combines the strengths of both discrete wavelet transform (DWT) and Double Deep Q Network (DDQN), is presented in the paper as an integrated mechanism that addresses the majority of these issues. It can dynamically adapt to the network, enhancing the Connected and Automated Vehicles (CAV) system’s safety and dependability. The dynamic approach is achieved by incorporating both the filtering and detection processes, which give a more robust and reliable performance output. Our numerical results clearly demonstrate the superior performance of DWT-DDQN over the existing conventional method at low and high levels of attack rates of α levels of 1% and 3%, and 5% and 7%.
Paper Presenters
Wednesday February 19, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

5:45pm GMT

Session Chair Remarks
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Virtual Room A London, United Kingdom

5:45pm GMT

Session Chair Remarks
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Virtual Room B London, United Kingdom

5:45pm GMT

Session Chair Remarks
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Virtual Room C London, United Kingdom

5:45pm GMT

Session Chair Remarks
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Wednesday February 19, 2025 5:45pm - 5:47pm GMT
Virtual Room D London, United Kingdom

5:47pm GMT

Closing Remarks
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Virtual Room A London, United Kingdom

5:47pm GMT

Closing Remarks
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Virtual Room B London, United Kingdom

5:47pm GMT

Closing Remarks
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Virtual Room C London, United Kingdom

5:47pm GMT

Closing Remarks
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Wednesday February 19, 2025 5:47pm - 5:50pm GMT
Virtual Room D London, United Kingdom
 
Thursday, February 20
 

9:28am GMT

Opening Remarks
Thursday February 20, 2025 9:28am - 9:30am GMT
Thursday February 20, 2025 9:28am - 9:30am GMT
Virtual Room A London, United Kingdom

9:28am GMT

Opening Remarks
Thursday February 20, 2025 9:28am - 9:30am GMT
Thursday February 20, 2025 9:28am - 9:30am GMT
Virtual Room B London, United Kingdom

9:28am GMT

Opening Remarks
Thursday February 20, 2025 9:28am - 9:30am GMT
Thursday February 20, 2025 9:28am - 9:30am GMT
Virtual Room C London, United Kingdom

9:28am GMT

Opening Remarks
Thursday February 20, 2025 9:28am - 9:30am GMT
Thursday February 20, 2025 9:28am - 9:30am GMT
Virtual Room D London, United Kingdom

9:28am GMT

Opening Remarks
Thursday February 20, 2025 9:28am - 9:30am GMT
Thursday February 20, 2025 9:28am - 9:30am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Advancements in Chronic Kidney Disease Prediction: A Comprehensive Review of ML Techniques and Integrated Methodologies
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - J R Harshavardhan, Anjan Kumar K N, Prasanna Kumar M
Abstract - Chronic Kidney Disease (CKD) is a pressing global health concern, where early diagnosis and effective management are vital to prevent progression to end-stage renal failure. This review paper analyzes advancements in the prediction and classification of CKD and related kidney disorders through machine learning (ML) techniques. It explores a spectrum of methodologies, ranging from traditional statistical models to advanced deep learning approaches, assessing their effectiveness in enhancing diagnostic accuracy. A key contribution of this work is the proposal of a novel methodology and block diagram for integrating diverse data sources, including patient demographics, clinical measurements, and medical images, to improve predictive outcomes. The proposed system leverages Convolutional Neural Networks (CNNs) for image analysis and employs ensemble methods for feature integration, aiming to optimize predictive performance. The review also addresses significant limitations, such as data quality and feature selection challenges, while emphasizing the advantages of early detection and personalized treatment through advanced ML models. By identifying research gaps and suggesting future directions, this paper aims to foster the development of more effective algorithms and real-time monitoring systems for CKD and kidney disorder management, ultimately contributing to improved patient outcomes.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Designing a Digital Twin for a Mixed Model Stochastic Assembly Line for the Reduction of Cycle Time
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Philane Tshabalala, Rangith B. Kuriakose
Abstract - The Fourth Industrial Revolution has had a significant and far-reaching impact on the manufacturing industry. A substantial transformation has taken place within the manufacturing industry, with a notable shift from the conventional approach of mass production to a more bespoke model driven by the global market's demand for enhanced product diversity. This requires the redesign of assembly lines to enable the production of multiple product variants, thereby increasing their complexity. In order to effectively manage the increased complexity and avoid potential bottlenecks caused by longer cycle times, it is essential to implement a virtual system capable of real-time monitoring and fault detection. The current methods for reducing cycle time are deficient in their lack of utilization of real-time data inputs. This article presents a case study of a water bottling plant that employs a mixed-model stochastic assembly line. Two virtual systems, a digital shadow and a digital twin, were developed using MATLAB and SIMULINK as potential solutions. The two systems processed the identical input data in order to calculate cycle times. The results of the study indicate that the application of real-time data and digital twins can lead to a significant reduction in cycle times in a mixed-model assembly line, with an average improvement of 19% in comparison to the digital shadow.
Paper Presenters
avatar for Philane Tshabalala

Philane Tshabalala

South Africa
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Stability in UAV Control Systems
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Hiep. L. Thi
Abstract - This paper explores the critical issue of stability in Unmanned Aerial Vehicle (UAV) control systems, particularly under varying environmental conditions and mission requirements. We discuss current challenges, including adaptive control, autonomous missions, urban navigation, and sensor integration. The paper also highlights recent advances in ensuring robust stability and outlines future research directions for improving UAV performance in complex and dynamic environments.
Paper Presenters
avatar for Hiep. L. Thi
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Strengthening the Role of Cooperatives in Indonesia's Economy: Challenges, Opportunities, and Strategic Frameworks
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Abdul Wahab Samad, Noerlina AnggIvanraeni, Khairul Ismed, Ivan Lilin Suryono, Zahera Mega Utama
Abstract - Cooperatives are a vital part of Indonesia's economy, and their growth and development have changed several times over the country's history. One of the cornerstones of any functional economy is the cooperative. Much headway was made towards assisting farmers during the New Order through the formation of Village Unit Cooperatives, often called Koperasi Unit Desa (KUD). Conversely, these cooperatives are encountering roadblocks and challenges in their development at the moment. In order to weigh the pros and cons of cooperatives, it is necessary to set up a cooperative framework that considers the cooperative movement and backs regulatory standards. Building this structure is a prerequisite to achieving this goal. That cooperative goods will be available for purchase and supported adequately in the future is ensured by embracing this paradigm. One of the quantitative research methodologies used for this examination was the Smart Partial Least Square (Smart PLS) analysis. An investigation was conducted to assess the scope of the opportunities and constraints that the cooperative market faces in its pursuit of integration into the Indonesian economy. The data found by the academic community at the Institute of Business and Informatics in 1957 shows that the p-value is less than 0.05, which means that there is a substantial association with a number higher than 0.7. This opens up a lot of possibilities for the development and expansion of cooperatives in Indonesia. It is possible that these cooperatives may form the bedrock of the country's future economic success
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

The Implementation of Intelligent Systems to Enhance Crisis Resilience in the Healthcare Sector: A Comparative Analysis with the Balanced Scorecard Method
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Paula Cristina De Almeida Marques, Paulo Alexandre Teixeira Faria de Oliveira
Abstract - This further underscore the need to have crisis resilience capabilities in a continuously evolving healthcare environment; especially in the wake of global crises such as COVID-19. This paper explores the impact of intelligent systems in the healthcare system. How to make resilient in crisis, and limits that analysis by comparing with Balanced Scorecard (BSC). When a hospital implements AI and ML technologies, it can dramatically enhance crisis surveillance, reduce the time needed for escalation predictions, and facilitate timely interventions accompanied by quick reactions to unexpected events. Based on multiple case studies, the literature review suggests that intelligent systems can greatly assist in resource optimization, operational efficiency improvement, and crisis decision making. Similarly, to how these perspectives are used to evaluate intelligent systems, BSC analyses them through four financial perspectives: customer; internal processes and growth and learning. We uncovered more than a billion euros respective and on average in value that could be gained fleet of 5G-enabled smart bicycles, specifically contribute to operational efficiency and clinical effectiveness in times of crisis by integrating smart systems. In addition to highlighting the importance of support from upper management an ongoing tailoring of smart systems to assist in the accomplishment of economic alignment to the strategic goals of healthcare organizations The Balanced — So, this study sets out that intelligent systems in health care with the Balanced Scorecard would provide a rapid response health system able to respond appropriately towards forthcoming crises. But there could be for policymakers and health care managers, provide incentives for the strategic integration of these technologies to support crisis management abilities, as well as more general benefits for human health.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Three-sided Energy Management Strategy of a PV-Wind-Battery Hybrid System with the Electric Vehicle Collaboration
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Asmae CHAKIR, Mohamed TABAA
Abstract - Throughout the world, the transportation and residential sectors are the most energy intensive. This continues to increase especially with the steady urban development. Consequently, the electricity consumption will increase especially in the above-mentioned sectors. To satisfy this demand, an increase of the electrical production is necessary in an environmentally friendly way. For this purpose, non-conventional or renewable generation is needed. But to overcome the intermittency, the concept of complementary sources hybridization has been launched. In this context, we considered the PV-Wind-Battery hybrid system in small scale that will supply a house already connected to the grid. This will remedy to the issue of increasing consumption in the residential sector. Regarding the transportation sector, a strategy to switch to electric transportation means has been initiated as well. To achieve this, we have hybridized our system with the existence of an electric vehicle used by the building's inhabitants as a means of transportation. On this paper, we proposed to manage the energy of this system according to three management sides, namely: source side, storage side and load management side. This combination allowed an optimization of the energy produced by the renewable system and a management of energy storage preference depending on the home's energetic states. Besides, the management system on the load side which helps in the minimization of the energy consumed trough the electricity utility during the periods of energy deficit to a consumption to satisfy just critical loads, especially with the presence of the mobile battery supplying the electric vehicle via the vehicle to home and home to vehicle strategy.
Paper Presenters
avatar for Asmae CHAKIR
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

3D Environmental Map for Navigational Safety in Autonomous Ship Operations
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Ayoung YANG, Atsushi ISHIBASHI, Ryota IMAI, Tsuyoshi MIYASHITA, Tadasuke FURUYA
Abstract - As interest in autonomous ship research grows and challenges from natural disasters increase, the accurate assessment of marine environments is becoming increasingly important. However, current marine environment assessments are primarily focused on evaluating marine resources and environmental conservation, with limited applicability to vessel navigation. This study proposes the creation of a 3D map that integrates both underwater and above-water data, specifically targeting key areas of vessel navigation. The above-water data were collected using LiDAR(Light Detection and Ranging), while the underwater data were mapped using multibeam sonar. This map offers a level of realism that is not achievable with traditional nautical charts, enhancing maritime safety and supporting the operation of autonomous ships through a new format of data.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Advancing Blended Learning Strategies: A Machine Learning Model for Predicting Student Success
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Selwa ELFIRDOUSSI, Hind KABAILI, Ghita SEKKAT
Abstract - The COVID-19 pandemic disrupted many sectors, including education. The confinement of administrative bodies, teachers, and students confronted us with an unavoidable reality: the need for distance learning. Once schools reopened, several countries and institutions began adopting blended learning models, combining both distance and face-to-face modes. This sudden shift revived research in the field of education, specifically what is known as "Educational Data Mining," a discipline aimed at developing new tools for extracting and utilizing educational data. This paper presents a Machine Learning Model aims to predict student performance in blended learning by understanding the impact of various social, economic, personal, and other factors on student performance, and to identify students at risk of failure.
Paper Presenters
avatar for Ghita SEKKAT
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

AI Technology in Auditing and Financial Error Detection
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Phuong Thao Nguyen
Abstract - Artificial Intelligence (AI) plays a transformative role in modern auditing by revolutionizing traditional methodologies and enhancing the overall audit process. The integration of AI technologies in auditing allows for the analysis of vast amounts of financial data, enabling auditors to identify anomalies, trends, and potential errors with unprecedented speed and precision. The significance of AI in identifying financial errors is paramount, as it enhances the detection of discrepancies that may go unnoticed through conventional auditing practices. By leveraging advanced algorithms and machine learning techniques, AI can recognize patterns and flag unusual transactions, thereby significantly reducing the risk of financial misstatements. Moreover, AI enhances the accuracy, efficiency, and compliance of financial audits. Automated data processing and real-time analytics minimize manual intervention, allowing auditors to focus on higher-level analysis and judgment-based tasks. AI tools also facilitate continuous auditing, enabling organizations to maintain compliance with regulatory standards and improve overall financial reporting. This paper provides an overview of the innovative ways AI is reshaping the auditing landscape, emphasizing its potential to elevate the quality and reliability of financial audits while streamlining processes and reducing costs.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Hybrid methods for detection of blood cancer images using support vector Machine
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Asmaa Abdul-Razzaq Al-Qaisi, Maryam Yaseen Abdullah, Enas Muzaffer Jamel, Raghad K. Abdulhassan
Abstract - New technologies, particularly in recent years, are revolutionising the way the world of cultural heritage, as well as museum and exhibition spaces, is understood. In this context, virtual reality (VR), in particular, is seen as a valuable tool to enrich and enhance traditional visits, using virtual elements to make visitors' experiences more engaging and interactive. Furthermore, as arousing emotions is a fundamental aspect in the creation of museum itineraries, VR techniques are flanked by physiological techniques such as electroencephalography (EEG) that allow for a comprehensive analysis of visitors' feelings. Using EEG-based indicators, this paper aims to analyse the emotional state of a sample of visitors engaged in a first physical, then virtual experience. Interaction, in this case, took place with five specially chosen objects belonging to the collection of the museum of handicrafts of Valle d’Aosta region in order to classify the different levels of involvement. The results suggest that EEG analysis contributed significantly to the understanding of emotional and cognitive processes in traditional and immersive experiences, highlighting the potential of VR technologies in enhancing participants' cognitive engagement.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Identification of Key Barriers to BIM Adoption for the Construction Sector: Specific to Asia
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Kannary Keth, Samia Ben Rajeb, Virak Han
Abstract - This paper presents a comprehensive literature review of research articles on Building Information Modeling in the past decade in thirteen Asian countries, including Cambodia, Thailand, Vietnam, Lao, Indonesia, Malaysia, Philippines, Singapore, Brunei, and Myanmar. Based on a Scopus search using keywords such as Building Information Modeling /Modelling /Model /Management /BIM, barrier/challenge, and the names of the 13 countries, the review identified 81 journal articles. Thirty-two articles were selected to extract the barrier statements. Only literature from four countries, China, Vietnam, Indonesia, and Malaysia, was found and selected. The semantic analysis by NVivo software included word frequency based on the literature review. As a result, 45 main barriers with six classifications: Cost, Technology, People, Environment, Organization, and Education were identified. Furthermore, the classification with high potential factors to influence the adoption of BIM in those countries is the environment, which demonstrates the external concerns, including standards, legality, guidelines, and regulations. Moreover, the main concern in China is a need for more willingness and awareness of BIM; in Vietnam, there is a lack of national standards; in Indonesia and Malaysia, there is concern about high costs. However, the study’s limitations include limited literature sources, exclusion of non-English sources, exclusion of article citations, and absence of expert validation.
Paper Presenters
avatar for Kannary Keth
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Real-Time Fall Detection with Transformers on a Customized System on Chip for High-Speed Efficiency
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Ivan Ursul
Abstract - This paper presents a comprehensive approach to real-time fall detection using advanced Transformer-based architectures tailored for deployment on resource-constrained devices. Our dataset, collected over four months using the WitMotion BWT61CL IMU and complemented by smartphone video recordings, provides a rich, multi-modal source for modelling fall and non-fall events in diverse environments. Our work focuses on the deployment and performance evaluation of three Transformer-based models—Standard Transformer, Performer, and Linformer— each optimized for latency and accuracy in processing timeseries accelerometer data. Rigorous data preprocessing, including noise filtering and feature extraction, was applied to enhance signal quality. We evaluate the models on a dataset comprising 403 samples, achieving a peak accuracy of 98% with the Standard Transformer, and competitive results of 96% with the Performer and Linformer. The Performer model emerges as the most efficient latency, achieving an average response time of 34ms, while the Standard Transformer and Linformer require 350ms and 110ms, respectively. This efficiency, combined with high sensitivity and specificity, underscores the Performer model’s suitability for real-time embedded systems. Our findings demonstrate that advanced Transformer models, with optimized hyperparameters and efficient architectures, can deliver accurate, low-latency fall detection solutions, paving the way for enhanced safety in applications requiring real-time monitoring on compact hardware.
Paper Presenters
avatar for Ivan Ursul

Ivan Ursul

Ukraine
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

An Analysis of Cross-Lingual Natural Language Processing for Low-Resource Languages
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Varsha Naik, Rajeswari K, Kshitij Jadhav, Aniket Rahalkar
Abstract - This study examines cross-lingual natural language processing (NLP) techniques to address the challenges of developing conversational AI systems for low-resource languages. These languages often lack extensive linguistic re- sources such as large-scale corpora, annotated datasets, and language-specific tools, making it difficult to capture the linguistic distinctions and contextual meaning essential for high-quality dialogue systems. This language gap restricts accessibility and inclusivity, preventing speakers of these underrepresented languages from fully benefiting from advancements in technology. The study compares various factors that affect model performance, including transformer model architecture, cross-lingual embeddings, fine-tuning strategies, and transfer learning approaches. Despite these challenges, the research shows that cross-lingual models offer promising solutions, especially when utilizing techniques like transfer learning and multilingual pre-training. By transferring knowledge from high-resource languages, these models can compensate for the scarcity of data in low-resource languages, enabling the development of more accurate, culturally sensitive, and inclusive AI systems. The findings highlight the importance of bridging linguistic divides to foster greater language diversity, accessibility, and technological inclusivity, ultimately supporting cultural preservation and revitalization.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Assessing Digital Innovation: Data from Digitally Skilled Teachers in Bisha Province, Saudi Arabia
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Elrasheed Ismail Mohommoud Zayid, Ahmad Mohammad Aldaleel, Omar Abdullah Omar Alshehri
Abstract - Machine learning classifiers are the first candidate methodology that could be used to assess the digital innovation across a set of teachers. This study aims to collect, build, represent, and discuss a reliable digital innovation skills (DIS) dataset by recruiting teachers chosen from the teachers who work in Bisha Province, Saudi Arabia. The study processed a rich data sample and made it accessible and shareable for the researchers' open use. DIS assessment addressed the problems and helped design a suitable innovation training module for the local community teachers. The total dataset comprises 400 conveniently collected data points, and each data point represents a complete record of teachers among the DSTs of Bisha Province. The research fields are prepared and set as fifty questionnaire questions, which distributed across the DSTs community in the area using social networks. Each question represents a single input or output feature for the classification model. Before running the ML models, the input variables are encoded serially from F0 to F49, and based on an explanatory test performed using LazyPredictools, only the positively contributing features are used. The extensive dataset, which is kept in the Mendeley Data repository, has a great deal of possibilities for reuse in sensitivity analysis, policymaking, and additional study. The decision tree, extra tree, and extreme gradient boosting (XGB) classifiers are examples of the recruited algorithms for evaluating DISs. The authors believe that this a wealthy kind of innovative respiratory dataset with its classification features will become a valuable mining source for interested researchers.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Business Information System Consultant Competences
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Malgorzata Pankowska
Abstract - Business information system (BIS) consultants are working on solving problems of client companies, providing them with high-quality services, helping them quickly respond to changes in their ecosystems, and to the changes initiated by new technologies. Client is usually the most important actor in the consulting process. Therefore, the consultants are to be well educated to ensure the best satisfying solutions. This study focuses on business information system analysts’ competences development to enable them participation in the consulting projects. In this study, the thematic review of literature was applied, the author’s framework of consultants’ competencies for business information system strategic analysis has been provided, and finally, the author formulate a recommendation on business analysis course for students of computer science at university. The findings indicate that both the students’ motivation, knowledge, experience, as well as a strong theoretical background and a methodological support from cooperative business units influence innovativeness and creativity of BIS consultants.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

CHURCH MANAGEMENT SYSTEM BASED ON MICRO SERVICE ARCHITECTURE AND CLOUD TECHNOLOGIES
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Martin Mayembe, Jackson Phiri
Abstract - Religious organizations, particularly church organisations, play a significant role in the lives of many people globally. These organisations require efficient management of various operations such as management of members, finances, events, and communications to fulfil their mission effectively. Existing church management systems are often built using traditional monolithic architectures, which come with inherent challenges. These challenges include platform dependence, limited scalability, and high upfront investment, making it difficult for many church organizations to develop, maintain, and scale their systems effectively and efficiently. This method of development is often referred to as the Spaghetti model. This study explores the application of the Micro-services Architecture in Church Management Systems, with the use of a service bus to enable communication between the services, to achieve modularity and scalability. To demonstrate the effectiveness of this design, a prototype is developed, focusing on two key modules: the Church Member Management System and the Financial Management System These modules work in tandem to manage member and associated member contribution data to provide access to up-to-date vital information.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Developing a photovoltaic fuel-less power generating system from mechanical waste: Implications for clean energy generation
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Williams A. Ayara, Adenike O. Boyo, Mustapha O. Adewusi, Razaq O. Kesinro, Mojisola R. Usikalu, Kehinde D. Oyeyemi
Abstract - The search for enhancing green electricity generation and the constant increase in the price of crude oil and its products propelled the choice of this research. Hence, a photovoltaic fuel-less power generating system using locally available materials. The input and output characteristics are analyzed to determine the efficiency, and the power generated by the photovoltaic-powered fuel-less generator is used to power an external load. The photovoltaic used is oriented to face in a direction with optimum tilt for maximum yield (to face southward) of solar power. This orientation and angle of tilt were determined using the Garmin Oregon450 GPS in conjunction with a Seaward Solar Survey 200R meter. Thus, the photovoltaic fuel-less generator was successfully developed. The driving component of this power-generating system is the 1 HP Direct Current (DC) motor, powered by two (2) 250 W mono-crystalline solar panels via a 12 V battery connected to a 30 A charge controller to maintain the charge level of the battery which helps to spin the 650 W Alternating Current (AC) alternator to deliver electricity. The device efficiently delivered power by lighting three (3) incandescent bulbs and a standing fan with total power between 100 – 220 W, and an efficiency of 70 -75%. This generator is eco-friendly since it does not emit any contaminants to the environment.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Fintech Sentiment Analysis using Deep Learning Models
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Sarah Anis, Mohamed Mabrouk, Mostafa Aref
Abstract - This research paper investigates the application of sentiment analysis in the fintech sector, focusing on stock market prediction through a transformer-based model, specifically FinBERT. By comparing its performance against established models like CNN, LSTM, and BERT across different datasets, the study demonstrates that FinBERT achieves superior accuracy in classifying sentiments from financial reviews. The findings emphasize the significance of specialized models tailored to specific domains for improving sentiment analysis within the financial sector, providing useful information for those involved in the fintech field.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Cloud-based Face Swapping Application
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Rotimi Williams Bello, Pius A. Owolawi, Chunling Tu, Etienne A. van Wyk
Abstract - One of the mainstream methods for user identification has been by face. However, the vulnerability of face-swapping applications to security issues when swapping the faces between two different facial images, has undermined the genuine aims of the technology, thereby threatening the security of certain applications and individual users when such action is performed without caution. To address this, we propose the development of scalable and safe cloud architecture for a face-swapping application that lets users upload two photos and get a face-swapped output. This is achieved by: (1) creating a secure virtual private cloud (VPC) to hold all application resources, (2) using a Web Application Firewall (WAF) to filter and safeguard requests, (3) putting application programming interface (API) Gateway into place to provide regulated access to the application's API, (4) processing and overseeing face-swapping operations with Lambda functions, (5) using VPC Endpoint to store input and output photos in Simple Storage Service (S3) buckets for private access, and (6) configuring a Simple Notification Service (SNS) to inform users of the progress and completion of their requests. A face swapping dataset derived from an open benchmark dataset was utilized for training and testing the proposed system. The experiment produced an effective solution with a 93% detection accuracy. The implications of this solution are: (1) the provision of security and private access to Amazon Web Services (AWS) by VPC Endpoints and WAF, (2) elimination of Network Address Translation (NAT) Gateway costs by utilizing VPC Endpoints for private S3 access, (3) offering of a scalable processing environment by Lambda functions without the need for server management, (4) delivering of real-time notifications by SNS to users regarding their request status, and (5) optimization of S3 storage ensures quick and efficient access to images.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Digital Leadership and Responsible Innovation: The Mediating Role of Digital Culture and Continuous Learning Environments
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Duong Bui, Cuong Nguyen
Abstract - Purpose - We are currently experiencing the era of digital transformation. This leads to concepts like digital leadership, continuous learning, and digital culture. The objective of this study is to investigate the influence of various dimensions of digital leadership (DL) on enhancing responsible innovation (RI), with a mediating role of continuous learning (CL) and the management of digital culture (DC). Design/methodology/approach – This study was employed a mixed-methods research design. Data were collected using a self-administered questionnaire distributed to a sample of 250 employees from small and medium-sized enterprises in Vietnam, selected through convenience sampling. Findings - Structural equation modeling was utilized for path analysis in the study. The findings indicated a positive and significant impact of digital leadership (DL) on responsible innovation (RI), mediated through the roles of continuous learning (CL) and digital culture (DC). Practical implications – This study has highlighted the significance of the impact of DL to create CL and DC in Vietnam. The study also confirmed the relationship between DL and RI. It adds to the evidence on digital leadership in Vietnam. Originality/value – Empirical evidence was provided by this study to support the role of DC in fostering RI. Furthermore, how DL strengthens its influence on CL and DC within organizations was demonstrated. By doing so, a critical gap in understanding the impact of DL on RI, CL, and DC in the context of Vietnam is addressed by this research.
Paper Presenters
avatar for Cuong Nguyen

Cuong Nguyen

Viet Nam
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Enhanced Feature Extraction and Representation in Hybrid CNN-ANN Architecture for Medical Image Classification using LC105K Dataset
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Suresh Rasappan, S. Ahamed Nishath, Francis Saviour Devaraj
Abstract - This study proposes a hybrid CNN-ANN architecture for lung cancer image classification on the LC105K dataset. Enhanced feature extraction and representation techniques improve classification accuracy. The model leverages CNN and ANN strengths, demonstrating superior performance compared to existing methods. Results show significant accuracy, precision, and recall improvements, offering a promising solution for computer-aided diagnosis.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Safeguarding Privacy of Sensitive E-Health Data Against AI Predictive Algorithm Threats
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Abdellah Tahenni, Abdelkader Belkhir
Abstract - This study provides the privacy concerns of AI predictive algorithms for E-health systems. A significant disadvantage is that these algorithms can infer delicate private health data of people, particularly high profile figures, from big datasets. This might be infringing privacy and result in discrimination or safety threats. The paper additionally analyzes the danger of AI prediction algorithms escalating wider privacy violation risks for patients and providers like accidental disclosure of personal details or unauthorized use of system vulnerabilities for information theft via AI models. The mixed-methods methodology encompasses evaluation of AI algorithm abilities, privacy breach case studies, expert interviews, healthcare provider surveys and eHealth method penetration tests. The results plot vulnerabilities, risk levels and technical, cultural and regulatory variables related to these privacy risks. To lessen those risks, a framework is suggested that has specialized safeguards including AI auditing and differing privacy, governance (data security policies and ethical AI guidelines), organizational (devoted privacy roles and staff training) along with ethical considerations balance innovation with privacy protection. Lastly, the study suggests multi-stakeholder, strategic and collaborative interaction among healthcare, policymakers, AI designers and patient advocates to mitigate AI driven privacy issues in eHealth systems through serious scrutiny and suggestions guided by this vision.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

The Impact of Affiliate Marketing and Gamification: Improving SMSE Business Sustainability
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Kevin Tanuwijaya, Elfindah Princes
Abstract - Indonesia's digital economy is rapidly expanding, fueled by technological advancements and government support for e-commerce. E-commerce has become a cornerstone of the nation's economy, significantly contributing to Gross Merchandise Value (GMV). However, Small and Medium-Sized Enterprises (SMSEs), crucial to Indonesia's economic landscape, face challenges in building lasting customer loyalty. This study investigates the impact of affiliate marketing and gamification on SMSE sustainability, focusing on economic, social, and environmental dimensions. Data were collected from a purposive sample of 100 respondents in Jakarta was analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS). The results demonstrate that both affiliate marketing and gamification directly enhance customer loyalty. Furthermore, customer loyalty was found to have a significant positive impact on SMSE sustainability. Crucially, the study reveals a mediating effect of customer loyalty, bridging the gap between affiliate marketing and gamification strategies and their ultimate impact on business sustainability. This research contributes valuable insights into the sustainable business literature by empirically examining its effects on key sustainability variables. The study concludes with a discussion of theoretical implications, practical recommendations for SMSEs, and avenues for future research.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

The Key Determinants of Live Streaming-Driven Online Purchasing Decision Among Generation Z
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Nguyen Quoc Cuong, Huynh Gia Nghi, Mai Thi Bich Ngoc
Abstract - Live streaming has transformed online purchasing, especially for tech-savvy Generation Z, who remain cautious in their purchasing decisions. This study explores the motivational factors driving live streaming-enabled purchasing decision in Vietnam. Applying CB-SEM in the Theory of Planned Behavior, this papers analyzes relationships among 07 variables: information quality, streamer attractiveness, interaction quality, trustworthiness, streamer expertise, online purchase intention, and online purchase decision. Samples consist of 233 Gen Z residents in Ho Chi Minh City (April–June 2024) was analyzed using SPSS and AMOS.. The findings indicate that all examined variables positively impact Gen Z's live stream purchase decisions, helping to advance scholarly understanding and offering insights for businesses to effectively integrate live streaming into their omnichannel strategies.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Digital Engagement and Work Life Balance: Job Performance of Generation Z Workers in the Hospitality Industry
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Deborah Prasetya Kusuma, Honey Paramitha Soetioso, Nurul Sukma Lestari
Abstract - The aim is to examine how employee engagement influences performance, considering the roles of technology and work-life balance. Furthermore, this research also evaluates job engagement as a mediating variable between digital engagement and work-life balance on job performance. This study utilizes quantitative methods, gathering data through both online and in-person questionnaire surveys. The data analyzed using partial least squares structural equation modeling (PLS SEM) and Smart PLS software. The participants are Generation Z hotel employees in Jakarta, such as contract or permanent staff, daily workers, and part-timers who are influenced by technology, referred to digital engagement. A total of 240 respondents successfully completed the survey. The results are digital engagement has influence on job performance, work life balance has not significant influence on job performance, digital engagement, work-life balance, mediated by job engagement has influence on job performance. This research presents a novel conceptual framework for analyzing hotel performance. It also provides valuable insights for hotel management to develop strategies that enhance generation z employees’ performance by improving digital engagement and work-life balance while simultaneously supporting the hotel’s sustainability. For further research can examine variables that were not included in this study, such as digital addiction, job stress, management support, job environment, and motivation with a broader reach local hotel or comparing even until international.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Enhanced Pose Face Recognition Using Multiple Adaptive Derivative Face Recognition (MADFR) and Ensemble Method MADBOOST
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Rayner Henry Pailus, Rayner Alfred
Abstract - Pose face recognition systems often struggle with the variability of illumination and face poses, especially when images are captured in uncontrolled environments. This paper addresses these challenges by proposing a novel face recognition approach: Multiple Adaptive Derivative Face Recognition (MADFR). Our method focuses on optimizing face recognition at every processing level to enhance overall accuracy. By incorporating multiple illumination training samples and diverse training data, including both controlled and wild images, our approach improves the robustness of face recognition models. Our analysis highlights the limitations of existing models like FaceNet, particularly in handling images with multiple face poses and varying background illuminations. We propose pose estimation landmarking and localization with multiple landmarks, which significantly enhances discriminant features. The effectiveness of our approach is demonstrated through extensive experiments on three datasets: LFW, Pointing 04, and Carl Dataset. Our results show that the proposed MADFR system, combined with the ensemble method MADBOOST, consistently outperforms other models. Specifically, MFRF 10 emerged as the top-performing model across all datasets, exhibiting high accuracy and low error rates. This research makes a significant contribution to the eld of face recognition by providing a robust solution that effectively handles the complexities of real-world scenarios. In conclusion, the MADFR system, with its optimized processing and decision-making capabilities, demonstrates substantial improvements in face recognition accuracy, paving the way for more reliable and effective face recognition technologies.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Expanding Technology-Enhanced Quality Improvement in Surveys (TEQUIS): New Visualization Techniques for Monitoring and Enhancing Web Survey Responses
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Sayaka Matsumoto, Kunihiko Takamatsu, Shotaro Imai, Tsunenori Inakura, Masao Mori
Abstract - In the context of higher education, Institutional Research (IR) has increasingly emphasized the use of data-driven tools such as student surveys to enhance educational practices and university operations. This study addresses challenges in managing and improving student surveys through advanced visualization techniques. We propose a third visualization method—a stacked bar graph—alongside two existing methods, the heatmap and bar graph with line overlay. This third method visually represents the progression of respondent dropout across questions, offering a detailed view of response continuity. The three visualization methods were used to compare pre- and post-improvement survey data, highlighting key factors such as question design and response behavior. The results indicate that reducing the number of questions and providing clear instructions significantly improve response rates, especially in the later sections of the surveys. The third visualization method effectively highlights these improvements by enabling precise monitoring of dropout trends and response continuity. This study situates its contributions within the interdisciplinary framework of Eduinformatics, integrating education and informatics to optimize educational processes. The proposed visualization methods offer practical tools for evaluating the quality of student surveys and ensuring the validity of collected data. While primarily aimed at student surveys, these methods have broader applicability to other survey-based research contexts.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Hybrid Wired-to-Wireless Architecture for In-Vehicle Communication: A Case Study on Headlamp Control Module
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Asmaa Berdigh, Kenza Oufaska, Khalid El Yassini
Abstract - This study proposes a gradual transition from cabled to wireless communication in vehicles as a means of reducing weight and meeting regulatory requirements related to CO2 emissions, maintenance costs, and time to market. However, the study recognizes that different network domains and compartments in the vehicle have varying requirements and constraints. Therefore, a hybrid architecture between classical wired and wireless networks using Ultra-Wideband (UWB) was proposed as a starting point for testing the feasibility and obtaining feedback. We selected the Headlamp Control Module (HCM) as an application domain since it represents a reduced network consisting of a microcontroller unit (MCU) that operates as a slave to another electronic control unit (ECU) and sensors. This allowed the study to apply the proposed approach to a representative unit scenario. The study outlines the system architectural description for the selected system, the HCM. It describes the Controller Area Network (CAN) and UWB communication and analyzes the requirements that must be fulfilled to interchange both communication technologies. This paper proposes a CAN-UWB gateway system architecture and simulates it to evaluate its ability to meet communication requirements.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Hyperchaotic Systems and Other Mathematical Constructs for Enhanced Image Cube Encryption
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Eyad Mamdouh, Mohamed Gabr, Marvy Badr Monir Mansour, Amr Aboshousha, Wassim Alexan, Dina Reda El-Damak
Abstract - This study presents an encryption algorithm for picture cubes that is based on complex differential equation-derived hyperchaotic systems. In order to enable efficient multidimensional encryption, the sensitivity to beginning conditions—a key component of chaos theory—has been extended into the hyperchaotic realm. The combination of DNA coding sequences with Linear Feedback Shift Registers (LFSRs) has increased the complexity of the method. The utilization of LFSRs provides secure pseudo-random sequences, whereas DNA coding adds more cryptographic depth. This combination has produced a strong encryption system that guarantees data security and resistance to sophisticated cryptanalysis attacks. The suggested encryption method has proven to be suitable for protecting volumetric picture data due to its superior performance in entropy, key sensitivity, and resilience to statistical attacks.
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

SUSTAINABILITY ON THE RESTAURANT INDUSTRY WITH THE INFLUENCE OF VISUAL PRODUCT, PACKAGING AWARENESS, BRAND AWARENESS AND BUYING DECISION
Thursday February 20, 2025 9:30am - 11:00am GMT
Authors - Berliana Tadjudin, Elencia, Davy Jivan Parmono, Tiurida Lily Anita
Abstract - There are factors that indirectly influence consumer purchasing decisions in the restaurant industry. As consumer awareness toward environmental issues grows, the implementation of eco-friendly packaging, environmentally friendly visual appeal and adopting sustainable business model are a growing trend in the restaurant industry. In other hand, elements such as the aesthetics of the menu, food packaging, the design of the restaurant room, and the general brand awareness are also one of the factors that play an important role in influencing purchasing decisions, which could also be a factor toward consumer trust in the restaurants and loyalty toward the business. With both ideas in mind, this research was conducted to answer and analyze the impact of various elements on consumer buying decisions toward a restaurant adapting sustainability model. The research is conducted in Greater Jakarta Region and manages to gather 250 samples of respondents which are analyzed statistically, to investigate the validity of the hypothesis. The data gathered from the analysis shows that there are significant relationships between the variables.
Paper Presenters
Thursday February 20, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

11:00am GMT

Session Chair Remarks
Thursday February 20, 2025 11:00am - 11:03am GMT
Thursday February 20, 2025 11:00am - 11:03am GMT
Virtual Room A London, United Kingdom

11:00am GMT

Session Chair Remarks
Thursday February 20, 2025 11:00am - 11:03am GMT
Thursday February 20, 2025 11:00am - 11:03am GMT
Virtual Room B London, United Kingdom

11:00am GMT

Session Chair Remarks
Thursday February 20, 2025 11:00am - 11:03am GMT
Thursday February 20, 2025 11:00am - 11:03am GMT
Virtual Room C London, United Kingdom

11:00am GMT

Session Chair Remarks
Thursday February 20, 2025 11:00am - 11:03am GMT
Thursday February 20, 2025 11:00am - 11:03am GMT
Virtual Room D London, United Kingdom

11:00am GMT

Session Chair Remarks
Thursday February 20, 2025 11:00am - 11:03am GMT
Thursday February 20, 2025 11:00am - 11:03am GMT
Virtual Room E London, United Kingdom

11:03am GMT

Closing Remarks
Thursday February 20, 2025 11:03am - 11:05am GMT
Thursday February 20, 2025 11:03am - 11:05am GMT
Virtual Room A London, United Kingdom

11:03am GMT

Closing Remarks
Thursday February 20, 2025 11:03am - 11:05am GMT
Thursday February 20, 2025 11:03am - 11:05am GMT
Virtual Room B London, United Kingdom

11:03am GMT

Closing Remarks
Thursday February 20, 2025 11:03am - 11:05am GMT
Thursday February 20, 2025 11:03am - 11:05am GMT
Virtual Room C London, United Kingdom

11:03am GMT

Closing Remarks
Thursday February 20, 2025 11:03am - 11:05am GMT
Thursday February 20, 2025 11:03am - 11:05am GMT
Virtual Room D London, United Kingdom

11:03am GMT

Closing Remarks
Thursday February 20, 2025 11:03am - 11:05am GMT
Thursday February 20, 2025 11:03am - 11:05am GMT
Virtual Room E London, United Kingdom

11:43am GMT

Opening Remarks
Thursday February 20, 2025 11:43am - 11:45am GMT
Thursday February 20, 2025 11:43am - 11:45am GMT
Virtual Room A London, United Kingdom

11:43am GMT

Opening Remarks
Thursday February 20, 2025 11:43am - 11:45am GMT
Thursday February 20, 2025 11:43am - 11:45am GMT
Virtual Room B London, United Kingdom

11:43am GMT

Opening Remarks
Thursday February 20, 2025 11:43am - 11:45am GMT
Thursday February 20, 2025 11:43am - 11:45am GMT
Virtual Room C London, United Kingdom

11:43am GMT

Opening Remarks
Thursday February 20, 2025 11:43am - 11:45am GMT
Thursday February 20, 2025 11:43am - 11:45am GMT
Virtual Room D London, United Kingdom

11:43am GMT

Opening Remarks
Thursday February 20, 2025 11:43am - 11:45am GMT
Thursday February 20, 2025 11:43am - 11:45am GMT
Virtual Room E London, United Kingdom

11:45am GMT

A Process Framework for Advancing Infrastructure Maturity Models in Wholesale Food Markets: A Pragmatic Approach
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Etian Ngobeni, Sara Grobbelaar, Christopher Mejia-Arguata
Abstract - Infrastructure maturity models largely guide an organization towards adopting advanced technologies. However, the knowledge on how such models can be developed for wholesale food markets is still lagging. This study fills the gap by using a pragmatic approach and the application of design science research methodology to develop a roadmap to developing a maturity model for infrastructure in wholesale food markets. This paper proposes a three-phase comprehensive framework for developing a maturity model.
Paper Presenters
avatar for Etian Ngobeni

Etian Ngobeni

South Africa
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Deep Learning Models for Low-Cost Air Quality Sensor Calibration
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Senthil Kumar Subramani Anandan, Lorenzo Garbagna, Lakshmi Babu Saheer, Mahdi Maktar Dar Oghaz
Abstract - Air quality monitoring systems have become an important part of urban areas due to recent attempts to monitor pollution levels to tackle problems such as climate change and population health risks. In recent years, research has been conducted of the utilisation of lowcost pollution concentration sensors to improve and expand on current air monitoring systems, as well as creating mobile systems that could be deployed in different scenarios. Although, the spread of Internet of Things (IoT) devices for monitoring systems brought the need of calibration between multiple different devices that could be found working inside the same network. This project explores the utilisation of Machine Learning and Deep Learning models to calibrate custom and Aeroqual sensors for PM2.5 and PM10 monitoring to an existing network from the city council in Cambridge, UK. For PM2.5, the collection with the custom sensor provided the highest accuracy when calibrated to the council one: Keras Regressor achieved an RMSE of 1.6240 and R2 of 0.8831, while with the data from Aeroqual a GRU Regressor achieved an RMSE of 1.9263 and R2 of 0.4867. On the other hand, collection with Aeroqual on PM10 concentration levels achieved an RMSE of 2.2087 and R2 of 0.6428 utilising RNN Regressor, while an MLP with Attention achieved a lower accuracy, with an RMSE of 4.9582 and R2 of 0.3297.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

DEVELOPMENT OF A WEB APPLICATION FOR POULTRY FARM MONITORING AND CONTROL SYSTEM
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Ferlyn P. Calanda
Abstract - The main goal of this project was to develop a Poultry Farm Monitoring and Control System using a web application platform. This system was designed to assist farmers by providing real-time data on temperature, humidity, ammonia levels and the overall environmental conditions within the poultry houses. As a result, farmers were able to access this information and make informed decisions to maintain animal welfare and productivity. The study employed a combination of descriptive and developmental research methods. A total of thirty (30) respondents including farmers, agriculturists, veterinarians, and faculty members from the agriculture department, took part in the study. The number of respondents was based on the suggestion of Jakob Nielsen [2012], which states that for quantitative studies, usability tests can be deployed on at least twenty (20) users to get statistically significant numbers. These respondents were able to remotely monitor the data and use it to inform decision-making processes.
Paper Presenters
avatar for Ferlyn P. Calanda
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Implementing Human-Machine Collaboration in an Industry 5.0 setting – a Case Study of an Automated Water Bottling Plant
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - J. Coetzer, R B Kuriakose, H J Vermaak
Abstract - As manufacturing and business sectors adopted Industry 4.0, the Fifth Industrial Revolution (Industry 5.0) emerged. Unlike its predecessor, Industry 5.0 extends its focus beyond economic growth and job creation, recognizing the manufacturing sector’s potential to support to broader societal goals. The continuous technological advancements and system improvements of Industry 5.0 have sparked a new area of research: enhancing human-machine interaction in commercial and industrial manufacturing environments by fostering better collaboration between humans and machines. There have been limited studies on how to establish a CDM process that takes into account the worker's recognition and ability to adapt to this development. The aim of the paper is to explore if existing protocols for Human-Machine Collaboration (HMC) are present in the manufacturing sector. If such protocols do not exist, the paper seeks to develop a universal protocol suitable for implementation in an Industry 5.0 context. An entirely mechanized water bottling plant will be serve as a case study to examine the effects of HMC. The study aims create a protocol that supports CDM within an Industry 5.0 environment. To support this goal, a single-case experiment has been conducted to test the theory of HMC that will lead to optimal production time of an automated system in an Industry 5.0 context. The paper details the background that motivated the research, methodology used and showcases steps taken in creating a protocol for CDM before concluding with the investigation of preliminary results, that show an up to an average of 24% reduction in production time.
Paper Presenters
avatar for J. Coetzer

J. Coetzer

South Africa
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Information and communication technology as an enabler of knowledge management at a South African University
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Vusumzi Funda, Bingwen Yan
Abstract - Knowledge is a strategic asset and a critical source of competitive advantage for organisations. Consequently, organisations employ various knowledge management (KM) enablers to acquire, store, secure, retrieve, share and utilize knowledge, all of which are crucial for enhancing organizational performance. Information and Communication Technologies (ICTs) play a pivotal role in facilitating these processes. This study aimed to evaluate the effectiveness of ICT usage in KM within the context of South Africa, with a specific focus on identifying barriers to ICT utilization. A quantitative method research approach was adopted using surveys. The findings revealed that the selected university lacked a comprehensive guideline on ICT usage, which hindered effective KM. The study concluded that while KM is essential at the University, significant efforts are needed to improve its practices. Additionally, a comparative methodology was proposed to analyse disparities in ICT utilization across different institutions. This study contributes valuable insights into KM and offers practical implications for policy review, potentially influencing management and other stakeholders to initiate necessary reforms.
Paper Presenters
avatar for Vusumzi Funda

Vusumzi Funda

South Africa
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

The Main Issues of Discrimination in the Workplace in Information Technology Organizations of Armenia
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Maria Sahakyan, Meri Badalyan, Lusine Karapetya
Abstract - The article is devoted to the study of the essence and characteristics of discrimination in the IT workplace. Obviously, the field of information technology is one of the priority areas for the development of the economy of the Republic of Armenia. This area is developing quite rapidly, and the average salary in IT companies is higher than the average salary in other spheres in Armenia. On the one hand, we still face the stereotype that a successful IT professional is a man. On the other hand, women in Armenia are starting to play an increasingly important role in coding, product development, web design, and other IT areas. The average share of women employed in IT in the world doesn't exceed 20% even though the tech world aspires to achieve gender balance and diversity. According to the data of 2022, more than 43% employees of the IT sector in Armenia are women, which is a quite high index at the global level. But still women in the IT sector earn on average about 1.5 times less than men. Despite the efforts of different engaged bodies to diminish the discrimination in the work-place, this is still a serious issue.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

A Comparative Analysis of Support Vector Machine, Random Forest, Neural Prophet, and Long Short-Term Memory Algorithms for Forecasting Rainfall in Zambia
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Lillian Mzyece, Jackson Phiri, Mayumbo Nyirenda
Abstract - Accurate rainfall forecasts are critical for various sectors, yet traditional methods struggle due to evolving and non-linear weather patterns. This study evaluates four machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), Neural Prophet (NP), and Long Short-Term Memory (LSTM)—to determine the most effective algorithm for rainfall forecasting in Zambia. Results show that Neural Prophet outperformed others, achieving the lowest RMSE (4.67), MAE (16.75), and MAPE (13.40%). Its autoregressive capabilities, interpretability, and reduced parameter complexity make Neural Prophet the preferred choice for forecasting rainfall trends in Zambia.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

An Optimized XGBoost for Pediatric Appendicitis Prediction
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Nailah Al-Madi
Abstract - The diagnosis of appendicitis is a challenge especially for children, as its symptoms overlap other diseases and children are unable to express their pain well. The misdiagnosis rate ranges from 28% to 57% in children. Machine learning is efficient in building models that can help predict diseases. XGBoost is one of the best machine learning models since it is based on ensemble learning approach. XGBoost has hyper-parameters that should be tuned well in order to achieve high performance. These parameters could be optimized to find the optimal or near optimal performance of XGBoost. In this paper, an Optimized- XGBoost model is proposed, which uses Genetic Algorithm to optimize seven parameters of XGBoost to achieve high performance. This Optimized-XGBoost is used to predict three class labels of pediatric Appendicitis, including diagnosis (appendicitis or no appendicitis), Severity (complicated or not complicated), and management(conservative or surgical). The experiments were implemented on Pediatric Appendicitis with 38 features and 780 records, and compared optimized-XGBoost with original XGBoost, and other well-known classifiers, such as DT, SVM, NB, KNN, RF, and Adaboost. Results show that optimized-XGBoost achieved highest results for accuracy, precision, recall and F1-Score. For example, the F1 score results for the prediction of severity is 96.15%, for the prediction of diagnosis is 99.36%, and for treatment is 99.36%.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Computer Vision as a Tool for Tracking Gastropod Chemical Trails
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Andre Viviers, Bertram Haskins, Reinhardt A Botha
Abstract - Tracking gastropod chemical trails is time-consuming and error-prone. This paper argues that computer vision provides a viable alternative. Using selected image manipulation and segmentation techniques, an unlabeled dataset was generated. A simple K-Means clustering algorithm and manual labelling created a labelled dataset. Thereafter, a best-effort model was trained to detect gastropods within images using this dataset. Using the model, a prototype was created to locate gastropods in a video feed and draw trace lines based on their movement. Five evaluation runs serve to gauge the prototype’s effectiveness. Videos with varying properties from the original dataset were purposefully chosen for each run. The prototype’s trace lines were compared to the original dataset’s human-drawn pathways. The versatility of the prototype is demonstrated in the final evaluation by generating fine-grained trace lines post-processing. This enables the plot to be adjusted to different parameters based on the characteristics that the resulting plot should have. This research demonstrated that a gastropod tracking solution based on computer vision can alleviate human effort.
Paper Presenters
avatar for Bertram Haskins

Bertram Haskins

South Africa
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Feasibility of the Cyber-Physical Nurse
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Maya Dimitrova, Nina Valchkova
Abstract - The paper presents the concept of a ‘cyber-physical nurse’ from a feasibility perspective for wider inclusion in healthcare, in particular in relation to empathic communication with the patient. The results of a pilot study on user perception of two robotic and one human faces are presented and discussed in this context. Users attributed positive features to neutral agents’ facial expressions, but not negative, which increases the feasibility of introducing social robots in healthcare. Some guidelines for cyber-physical nurse design are discussed, addressing challenges to its possible implementation in hospitals, rehabilitation centers, and home care settings.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

From gas sensors to efficient electronic nose systems: A bibliometric analysis to short survey
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Tagnon Adechina Geoffroy Zannou, Semevo Arnaud Roland Martial Ahouandjinou, Manhougbe Probus Aymard Farel Kiki, Adote Francois-Xavier Ametepe
Abstract - Sensor-based gas analysis has been the subject of much research, particularly in the development of electronic nose (e-nose) systems. E-noses are based on chemical sensors to detect and analyze volatile organic compounds, and thus play an important role in a variety of fields. In this paper, based on three research strings, we have performed a bibliometric analysis to examine current trends and scientific contributions in the field of sensors for detecting odors and volatile organic compounds (VOCs), their use in electronic nose systems, work to improve their performance and their optimization. Using the Scopus database and English-language documents published between 2014 and 2024, we identify the most prolific authors, countries and journals in these fields. After that, a short literature review provides a detailed overview of the strategies to improve e-noses selectivity and reduce their drift. The results of the bibliometric analysis show a growing intercontinental interest, with strong scientific activity in China, the United States, India and Italy, with a particularly strong focus on performance improvement and sensor optimization. The short survey reveals the existence of a wide range of gas sensors with their advantages and disadvantages, significant advances in improving the performance of sensors and electronic noses, as well as new challenges that deserve attention.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

The Triangulation Study on Islamic Marketing of Full-fledged Islamic Banks
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Adibah Alawiah Osman, Azwan Abdullah, Sharul Shahida Shakrein Safian, Nor Zawani Ibrahim, Nik Rozila Nik Mohd Masdek, Norhasimah Shaharuddin, Nur Athirah Sumardi
Abstract - The process of using various data methods within one study to con-firm that the results are firmly supported by the predictions made is called triangulation. Several methodological debates have highlighted the limitations of quantitative research compared to qualitative research. This paper hunts to explore the triangulation research approach in the background of Islamic marketing at fully established Islamic banks in Malaysia. Islamic marketing of Islamic banks is defined as the application of Islamic banking knowledge, Islamic advertising ethics, and the augmentation of learning and instruction by Islamic bank employees in this study. The present research clarifies the basis links between the quantitative data of Islamic bank’s staffs at fully established Islamic banks and the qualitative insights of Islamic financial experts. The amalgamation of both qualitative and quantitative approaches in data collection and evaluation significantly improves the quality of the research outcomes. Further studies ought to explore the application of the triangulation method in other domains.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Accessibility for the Elderly: a methodology for comparing objective and subjective measures regarding health and transportation services
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Carlo Giuseppe Pirrone
Abstract - This study explores urban accessibility for the elderly, focusing on the importance of integrating objective and subjective measures for a comprehensive assessment. Objective measures, such as cumulative opportunity measures (CUM) or measurable travel times, quantify spatial data while often neglecting personal experiences and user perceptions. Subjective measures, obtained through surveys, become crucial in defining the ease of access to services, including satisfaction levels derived from the journey, barriers due to individual factors (age, health, disability), as well as comfort and safety. A combined methodology would promote a new interpretation of urban accessibility. A case study conducted in Rende, Italy, illustrates a practical application by mapping healthcare services and public transport to assess pedestrian accessibility. A pilot survey gathered elderly residents' perceptions of distance, travel time, and service satisfaction. Preliminary results indicate a reluctance to walk, overestimated perceived distances and a strong reliance on private vehicles, highlighting the need for infrastructures and services to provide a better connection of the elderly to healthcare services. Ongoing research will further refine the study by adapting objective measures to local perceptions to develop a specific accessibility indicator for the area.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Comprehensive IoT Solution for Improved Remote Monitoring and Safety in Outdoor Settings
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Dilshan De Silva, Dulaj Dawlagala
Abstract - The research demonstrates a reliable and efficient IoT solution aimed good at improving the safety of campers and hikers in outdoor settings. It consists of one primary device and a set of other peripherals, which employ LoRa communication, GPS positioning, and environmental parameters for location-based services. The primary device system consists of an ESP32 microcontroller fitted with a LoRa 433 MHz module and NEO-7MGPS powered using SYN-ACK protocol that allows the device to constantly communicate with the subordinate devices. The subordinate devices which are also based on ESP32 has LoRa modules and OLED screen for the purposes of receiving and providing information about the geofences and locational alerts. A significant strength of this system is that it is able to function in remote areas by bringing all the devices together into a mesh network so that data and devices can be synchronized without relying on the internet for connection purposes. Both primary and subordinate devices have the ability to connect to the internet wherever possible, update messages, synchronize, and transfer messages efficiently. Failure is further minimized for effective communication and precise positioning which are important in the management of outdoor safety hazards. The first prototype tests have shown the ability of the system to solve problems such as real-time interaction, data integration, and functioning in difficult environments. To npm and deploy the research further developed IoT systems for outdoor applications, effective outdoor deployment strategies were developed. As the next step, the system will be tested on a larger scale to assess its scalability, and user-centered interfaces will be redesigned to accommodate real-world scenarios.
Paper Presenters
avatar for Dulaj Dawlagala
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Examining the Impact of Data Governance on Privacy Regulations Compliance: A Systematic Literature Review
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Jude Osakwe, Sinte Mutelo, Nelson Osakwe
Abstract - This study aimed to investigate the interrelationship between data governance and compliance with privacy regulations. A systematic literature review was conducted to synthesise the existing research on data governance's impact on compliance with privacy regulations. The study found that data governance has a positive association with compliance, with integrated data governance methods and processes supporting decision-making, and stakeholders' involvement guaranteeing transparent processes. The findings also suggested that the impact of data governance on privacy regulations compliance needs a certain maturity level and top management support. Key recommendations for organisations are outlined to enhance their governance frameworks, promote transparency, and align resources effectively in order to bolster compliance with privacy regulations. The study concludes by addressing identified research gaps and offering directions for future studies aimed at exploring the evolving landscape of data governance and privacy compliance.
Paper Presenters
avatar for Sinte Mutelo
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Explainable AI Uncovers Key Clinical Factors Linked to Survival in Skin Cutaneous Melanoma Patients
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Nabeela Kausar, Ramiza Ashraf, Saeed Nawaz Khan
Abstract - Skin cutaneous melanoma is one of the most aggressive forms of skin cancer, with various prognostic factors that can significantly impact patients’ survival outcomes. Survival analysis helps in identifying key factors influencing patient outcomes and guides in clinical decision-making. In literature, statistical methods have been used for the survival analysis of skin cancer patients but these methods have limitations. To address the limitations of traditional statistical methods in survival analysis, researchers have developed a range of machine learning (ML) based survival analysis techniques. These ML techniques offer advanced capabilities for modeling complex relationships and improving prediction accuracy. But "black box" nature of ML models poses a challenge, especially in fields like healthcare where understanding the rationale behind predictions is crucial. It this work, Explainable AI (XAI) based survival analysis has been carried out using XGboost model and clinical features of skin cutaneous melanoma patients. XAI models explain their prediction by showing the important features involved in the prediction to demonstrate their reliability to be used by the clinicians. To validate the performance of XAI model, in this work, multivariate regression based Cox Proportional Hazard (CPH) model has been developed which shows the relationship of patients’ clinical features and survival time. The pro-posed XAI based model has C-index value of 84.3% and shows that age, pathology T stage, and pathology N stage are key factors influencing the survival of skin cutaneous melanoma patients. The CPH model further validates the strong association between these features and patient survival.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Hybrid Prediction Model using Elastic Regression and Echo State Networks for Enhanced Yield Prediction in Crop Systems: A Comparative Study of Dense Neural Networks and Hybrid Echo State Network Models
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Mulima Chibuye, Jackson Phiri
Abstract - Agricultural systems have been modeled and prediction of yields used in the space since the beginning of agriculture ,improvements in the crop science and better tools made the task much easier through the ages, from using the position of the sun to determining that certain weeds signify a good harvest to actually determining what factors precede observable phenomena, the space has be-come so advanced such that we are able to build better prediction models and the promise of quantum computation that can model much more complex systems and interactions among the individual parameters within the system promise to make us predict yields of crops with much better accuracy than has ever been deemed feasible. With the technology that we have available now, we can apply properties of physical systems on classical computers such as mimicking chaos theory to add randomness to our predictions as that is the way nature works. That randomness is due to how initial conditions might potentially fluctuate and we would normally call it random because we are missing certain parameters that if we collect, would greatly improve how we predict physical chaotic systems. The aim of this work is to explore how we can incorporate chaos in agricultural systems by making use of a hybrid approach to known systems like dense neural networks and more recent methods such as Echo State Networks.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Vehicle Detection and classification using YOLOv8 and YOLOv10: A Comparative Analysis of Model Performance and Metrics on the Novel VINC dataset
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Cynthia Sherin B, Poovammal E
Abstract - Vehicle detection and classification has a key role in evolution of intelligent transportation system. Accuracy in the detection enhances the efficiency of intelligent traffic monitoring systems. The paper shows a comparative study on the performance of YOLOv8 and YOLOv10 detection models on the novel Vehicle Identification aNd Classification (VINC) dataset introduced. They detect multi-class vehicles such as cars, trucks, buses, bicycles and bikes. The achievements of each model are assessed using precision, recall, F1 score and confusion matrices. The experimental results demonstrates the supremacy of YOLOv10 in detecting very small and more complex vehicle structures in the traffic scenario than YOLOv8. Alternatively, YOLOv8 also exhibited equivalent detection accuracy in detecting large vehicles like buses and trucks, by capturing the minute variations in the processed features. The detection models achieve precision of 97.2% and 93.6% for YOLOv10 and YOLOv8 respectively. YOLOv10 achieves high recall rate and F1 score of 92.4% and 81.4% respectively. Thus, the detection performance of these vehicles expresses the robust characteristics of both the YOLO versions. This research paper delineates the merits and drawbacks of these two versions on real-time circumstances, thereby creating faster and precise detection models.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Enhancing Transport Efficiency through Predictive Maintenance:A Machine Learning Approach Using NASA Turbofan Jet Engine Dataset
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Ansh Soni, Krish Modi, Aneri Shah, Nishant Doshi
Abstract - A successful and efficient transportation system depends on the credibility of engines and machinery. With the help of NASA Turbofan Jet Engine dataset, this paper focuses on the predictive maintenance framework to boost transport efficiency by leveraging sensor data. With the help of machine learning algorithms, we predict the Remaining Useful Life (RUL) of engine components based on training the model with appropriate algorithms that prompt scheduled services and maintenance to reduce downtime. Feature engineering techniques and predictions of RUL, Health Index(HI), and degradation score- the proposed model provides a methodology for enhancing system dependability and minimizing maintenance costs. This study provides valuable insights into current transportation setbacks.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Innovative tools in nursing education: the impact of Serious Games and ChatGPT on Instructional Design
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Dario Lombardi
Abstract - This study investigates the impact of Serious Games (SGs) and ChatGPT on nursing education, focusing on usability, learning outcomes, engagement, skill transfer, and future usage intentions. Using a multi-phase design aligned with the ADDIE instructional model, the study explores how these tools facilitate learning through experiential and cognitive pathways. In the first phase, students designed instructional interventions using the ADDIE model, while the second phase introduced ChatGPT as an AI-driven support tool. Findings reveal that SGs promote experiential learning by enhancing clinical skills, emergency response, and procedure retention. Usability metrics were high, with 79% of participants rating interface intuitiveness positively. Conversely, ChatGPT supported cognitive scaffolding, enabling faster and more effective instructional design. Students reported a significant increase in familiarity with AI, with 68.4% moving from "low" to "medium" familiarity. Engagement and motivation were strong for both tools, with 84.2% of participants intending to continue using ChatGPT. Despite its small sample size (n=19), this study highlights the potential for hybrid models that integrate SGs and AI to improve nursing education. It calls for the incorporation of these tools into curricula, emphasizing experiential learning (SGs) and cognitive support (AI), thereby addressing both procedural and conceptual learning needs.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Page Level Recognition and Reordering of Handwritten Documents: A Review
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Ravichandran S, R Kasturi Rangan, Manjesh R, S Karthik, T N Hemanth
Abstract - The page-level recognition and reordering of handwritten documents is important in digitizing and archiving systems. These systems focuses on solving the two problems of converting relevant parts of a handwriting document into recognizable formats for machines as well as correctly sequencing pages in order to preserve context. Building upon the state-of-the-art in Optical Character Recognition (OCR) and Document Layout Analysis (DLA), The paper suggests that these methods are effective for page-level text recognition that combines automatic reading order detection with advanced OCR modeling. This study evaluates the impact of a hybrid architecture combining Vision Transformers (ViT) for powerful feature extraction, and transformer-based Language Models (LMs) to provide context during text decoding. We then pose the task of reordering as a sorting problem and use a pairwise order-relation operator trained from annotated data to generalize to various layouts of input documents. The phenomenon under study reveals significant trends in the state-of-the-art performance on standard datasets with significant recognition accuracy gain and reordering precision. It opens up the efficient processing of handwritten documents in applications that range from preserving historical writing samples to today’s administrational scanned handwritten documents.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Principal Component Analysis of ICT Adoption among Students in Developing Countries
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Khanyisani I Ndlovu, Timothy T Adeliyi, Alveen Singh
Abstract - Integrating Information and Communication Technology (ICT) in educational settings has become fundamental to modern pedagogy. Despite its significant contributions, ICT's adoption, integration, and usability pose challenges for school students in developing countries, particularly in Sub-Saharan Africa. This study aims to identify and analyse the diverse factors influencing the widespread adoption of ICT among students. A comprehensive systematic literature review revealed thirty-eight factors from eighty-four articles that either facilitate or hinder ICT integration in students' academic activities. Using Principal Component Analysis (PCA) a statistical method known for reducing dimensionality and uncovering patterns in complex datasets. The study extracts the most critical factors impacting ICT adoption. The findings indicate that in addition to over-digitalization, cognitive barriers, health issues, time constraints, funding limitations, and a lack of modern software are significant factors affecting students' engagement with technology. The implications of this study are relevant to policymakers, educators, and academic institutions, providing a data-driven basis for strategies aimed at improving ICT adoption
Paper Presenters
avatar for Khanyisani I Ndlovu
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Professionalism of Expert Staff in the Indonesian House of Representatives (DPR RI) from a Public Policy Perspective
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Pesta Evaria Simbolon, Himsar Silaban, Khasan Effendi, T. Herry Racmatsyah
Abstract - Currently, the professionalism of expert staff at the House of Representatives (DPR RI) is influenced by various interconnected factors such as work management, coordination, compensation, qualifications, and competence. This study aims to identify the key factors affecting the professionalism of expert staff at DPR RI and provide recommendations to improve their performance. A qualitative research method was employed using thematic analysis, grounded theory, and triangulation through NVIVO 14 to analyze interview data and relevant documents. The results indicate that work management and coordination, competitive compensation, staff qualifications and competencies, rigorous recruitment and selection processes, and objective performance evaluations are the primary factors influencing professionalism. Furthermore, professional development and clear career paths enhance staff loyalty and performance, while benchmarking against industry standards offers insights into best practices that can be adopted. Addressing challenges and barriers in the workplace and ensuring a clear organizational structure help define roles and responsibilities. High-quality support from expert staff and satisfaction with compensation has a direct impact on the effectiveness of DPR RI. Thus, DPR RI must focus on and improve these factors to ensure the optimal efficiency and effectiveness of expert staff, ultimately supporting the overall performance and effectiveness of DPR RI.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Supporting Internships of Pre-Service Teachers by Digital Platform
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Ilnar Yarullin, Ramis Nasibullov, Almaz Galimov, Shamil Sheymardanov
Abstract - This paper is important because it highlights the need to improve how we help students with their internships. This need arises from higher expectations for the quality of training future teachers receive and the limitations of traditional methods of internship support. The article explains the idea and functioning of a digital platform designed to assist students with their internships. The aim of this platform is to improve the quality of their professional training by enhancing communication among everyone involved in the educational process. The software described provides options for creating flexible schedules that consider different types of internships, such as in-person, remote, and hybrid. This flexibility is especially important when working with various groups of students. In addition to assigning mentors and supervisors, both teachers and students can outline the goals and objectives of the internship. This helps to avoid misunderstandings and ensures that everyone is clear about what needs to be accomplished at each stage of the internship. The platform is connected to the university's corporate system, teaching websites, and electronic publications. As a result, teachers, students, and administrators can share information more easily.
Paper Presenters
avatar for Shamil Sheymardanov

Shamil Sheymardanov

Russian Federation
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

A Multi-Phase Method for Computer-Aided Diagnosis in Chest X-Rays Using Convolutional Neural Networks Transfer Learning and Multi-Label Classification
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Pablo Salamea, Remigio Hurtado, Rodolfo Bojorque
Abstract - Recent advancements in deep learning have enabled the development of convolutional neural network (CNN) architectures, which have proven to be valuable tools in computer-aided diagnosis (CAD) systems. These systems assist radiologists in identifying regions of interest associated with pathologies in chest X-ray images, a diagnostic tool recognized as essential by the World Health Organization (WHO). The WHO highlights that chest X-rays are an accessible and cost-effective method, crucial for evaluating respiratory and thoracic diseases, particularly in resource-limited settings and during global health emergencies. In this study, the Vindr-CXR dataset was used, known for providing labeled chest X-ray images suitable for multi-label classification tasks. The process began with data preparation, where images and labels were grouped in a binary format and split into training and validation sets. Subsequently, pre-trained neural network architectures, such as VGG16, InceptionV3, ResNet50, and EfficientNetB0, were utilized with weights initialized from ImageNet. The initial layers of these architectures were frozen, and dense layers with sigmoid activation were added for multilabel classification. During training, the binary crossentropy loss function and the Adam optimizer were employed. The models were trained for a fixed number of epochs, with validation conducted at the end of each epoch to evaluate metrics such as accuracy and loss. Finally, predictions were generated on the validation set, and key metrics such as the ROC curve, precision, recall, and F1-Score were calculated. The models achieved a promising performance, with an accuracy of 0.72 in detecting thoracic pathologies. These findings highlight the potential of deep learning to enhance diagnostic precision and support clinical decision-making, reaffirming the critical role of chest X-rays
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

A Study to Analyze the Effects of Music and Meditation on Attention and Emotion Using EEG Technology
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Pei-Jung Lin, Meng-Chian Wu,Jen-Wei Chang
Abstract - To compare the effects of music and meditation on brainwave patterns and attention, this study designed a series of EEG-based experiments. Participants were instructed to either listen to music or engage in meditation, while their attention levels were assessed using a Rapid Serial Visual Presentation (RSVP) paradigm to validate brainwave differences under varying attentional states. EEG data were collected to analyze changes in attention during exposure to different types of music. Subsequently, mathematical computations were applied to quantify and summarize the pre- and post-intervention differences. The experimental results revealed significant differences in the impact of various music genres on attention. Listening to classical music effectively enhanced attention, whereas listening to popular music demonstrated a notable effect on emotional relaxation. Deep meditation yielded the greatest improvement in concentration, and its brainwave patterns closely resembled those observed when listening to classical music. An analysis of Arousal and Valence metrics indicated that meditation led to positive emotional changes. These findings suggest that both music and meditation can influence attention and emotional states.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Estimating Forest Carbon Stocks: A Review of Above-Ground Biomass Methods
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - John Khoo, Rayner Alfred, Khalifa Chekima, Rayner Pailus, Chin Kim On, Ervin Gubin Moung, Raymond Alfred, Oliver Valentine Eboy, Normah Awang Besar Raffie, Ashraf Osman Ibrahim, Nosius Luaran
Abstract - Carbon stock serves as a crucial metric for assessing the quantity of carbon stored within terrestrial and aquatic ecosystems, exerting signicant inuence on global carbon dynamics and climate change mitigation eorts. Eective management of carbon stocks is vital for regulating atmospheric carbon dioxide (CO2) levels and mitigating the adverse impacts of climate change. The study delves into the estimation of carbon stocks, particularly focusing on above-ground biomass (AGB) as a key component of carbon storage in forests. In addition, explores methods for estimating above-ground biomass (AGB) of carbon storage in forests. Traditional eld-based approaches, statistical methods like regression, and machine learning techniques such as deep learning oer varied strategies for AGB estimation. These methods leverage a variety of data to enhance accuracy and scalability. Through empirical examples, the study presents their eectiveness in informing conservation strategies and fostering sustainable development amidst environmental challenges.
Paper Presenters
avatar for Rayner Alfred
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

Hybrid Machine Learning Models for Driver Fatigue Detection Using EEG and EOG Signals
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Juan Dominguez, Carlos Carranco, Remigio Hurtado, Rodolfo Bojorque
Abstract - Driver fatigue is one of the leading causes of road accidents worldwide, affecting concentration, reaction time, and vehicle control. Sleep deprivation, long driving hours, and monotonous conditions increase the risk, particularly among professional drivers and shift workers. Identifying early signs of fatigue is essential for improving road safety and preventing accidents. This study introduces a structured framework for detecting fatigue based on EEG and EOG signal analysis. Using the SEED-VIG dataset, the methodology integrates multiple stages, including data processing, feature selection, model training, and performance optimization. Various machine learning models were tested, with particular emphasis on Random Forest, LSTM networks, and ensemble techniques such as Gradient Boosting, XGBoost, and LightGBM. Additionally, explainability techniques like SHAP and LIME were applied to highlight critical fatigue indicators, such as variations in blink frequency, saccadic movements, and brainwave activity in the theta and delta frequency bands. Among the tested models, the optimized Random Forest approach yielded the highest accuracy, with an RMSE of 0.0257. These findings contribute to the advancement of fatigue monitoring technologies, offering practical solutions for real-time driver assessment and accident prevention.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

11:45am GMT

In-band noise reduction from PCG Signal using Dabuchies-wavelets based approach
Thursday February 20, 2025 11:45am - 1:15pm GMT
Authors - Madhwendra Nath, Subodh Srivastava
Abstract - Denoising of the heart sound signal is crucial part of the heart sound signal analysis, as it reduces the interfering noise such as respiration noise, gastric noise, speech, motion artifacts, and power-line interference from the signal. The In-band noise in a phonocardiogram (PCG) signal refers to noise or artifacts that overlap with the frequency range of interest for major heart sounds which is typically 20–100 Hz. To reduce this in-band noise; a Daubechies-wavelets based approach is proposed. The parameters of Dabuchies-wavelets are revamped. To judge the proficiency of the proposed method, a novel performance-metric-index, Noise-area-difference (NAD) has been introduced. It evaluates the Denoising performance. The proposed method is compared with three other existing methods. The comparison results reveal that the proposed method outperforms existing state-of-the-art Denoising of Heart sound signals.
Paper Presenters
Thursday February 20, 2025 11:45am - 1:15pm GMT
Virtual Room E London, United Kingdom

1:15pm GMT

Session Chair Remarks
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Virtual Room A London, United Kingdom

1:15pm GMT

Session Chair Remarks
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Virtual Room B London, United Kingdom

1:15pm GMT

Session Chair Remarks
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Virtual Room C London, United Kingdom

1:15pm GMT

Session Chair Remarks
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Virtual Room D London, United Kingdom

1:15pm GMT

Session Chair Remarks
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Thursday February 20, 2025 1:15pm - 1:17pm GMT
Virtual Room E London, United Kingdom

1:17pm GMT

Closing Remarks
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Virtual Room A London, United Kingdom

1:17pm GMT

Closing Remarks
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Virtual Room B London, United Kingdom

1:17pm GMT

Closing Remarks
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Virtual Room C London, United Kingdom

1:17pm GMT

Closing Remarks
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Virtual Room D London, United Kingdom

1:17pm GMT

Closing Remarks
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Thursday February 20, 2025 1:17pm - 1:20pm GMT
Virtual Room E London, United Kingdom

1:58pm GMT

Opening Remarks
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Virtual Room A London, United Kingdom

1:58pm GMT

Opening Remarks
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Virtual Room B London, United Kingdom

1:58pm GMT

Opening Remarks
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Virtual Room C London, United Kingdom

1:58pm GMT

Opening Remarks
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Thursday February 20, 2025 1:58pm - 2:00pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

A Comprehensive Analysis of Social Franchising Model Development: Exploring Key Dimensions and Insights
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Welekazi Ntloko, Sara S. (Saartjie) Grobbelaar
Abstract - Social franchising is a business model in which a successful social enterprise is replicated in multiple areas, often by providing franchisees with training, support, and resources. Social franchising aims to assist social entrepreneurs to impact a larger number of people with their services by scaling their operations while maintaining their standards of excellence and consistency. Social franchising (SF) is used to scale social business models in new locations, allowing them to expand their impact. This article serves to analyse and review the literature surrounding social franchising. Preliminary results reveal a substantial focus on healthcare in social franchising research, with limited multidisciplinary studies. Challenges include the limited legal frameworks in many jurisdictions, impacting stakeholder certainty. The study aims to contribute insights into the evolving landscape of social franchising, emphasizing the intersection with SBMs and HO for sustainable and impactful outcomes, with potential implications for sustainable economic and social development.
Paper Presenters
avatar for Welekazi Ntloko

Welekazi Ntloko

South Africa
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

A Proposal for A Multi-factor Authentication Scheme to Prevent Wi-Fi Hacking at a University
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Kuhlula Mathebula, Noluntu Mpekoa, Khutso Lebea
Abstract - This research aims to assess the suitability of a multi-factor authentication (MFA) scheme for protecting a university's Wi-Fi network from threat actors. Given the vulnerabilities of current single-factor authentication methods, which often rely on usernames and passwords, implementing MFA is proposed as a more secure alternative. MFA enhances security by requiring users to pass through multiple authentication mechanisms, such as knowledge-based, possession-based, and biometric methods, making unauthorised access significantly more difficult. The research seeks to determine the most effective combination of authentication factors for a university environment. The research findings may have broader implications for securing educational institutions' networks.
Paper Presenters
avatar for Kuhlula Mathebula

Kuhlula Mathebula

South Africa
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Investigating Fake News Detection Using BERT/RoBERTa LLMs
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Amr Abu Alhaj, Omar Safwat, Youssef Ghoneim, Imran Zualkernan, Ali Reza Sajun
Abstract - This paper examines the use of pre-trained models like Bidirectional Encoder Representations from Transformers (BERT) and A Robustly Optimized BERT Pretraining Approach (RoBERTa) to create reliable models for detecting fake news from media articles. Traditional Machine Learning (ML) methods frequently have difficulties in accurately identifying the nuances of misinformation due to extensive feature engineering dependencies. The latest advancements in Large Language Models (LLMs) such as BERT and RoBERTa have fundamentally transformed misinformation detection by providing deep context. The research utilizes the LIAR dataset, containing 12.8k manually labeled statements from PolitiFact.com, along with associated metadata and speaker credit scores. The approach combines BERT/RoBERTa embeddings with complementary architectures for binary classification, introducing a credit-score calculation reflecting speakers’ historical truthfulness. Notably, BERT-BiLSTM-CNN-FC and RoBERTa-BiLSTM-CNNFC configurations achieved state-of-the-art F1-scores of 0.76 and 0.74, respectively.
Paper Presenters
avatar for Amr Abu Alhaj

Amr Abu Alhaj

United Arab Emirates
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Machine Learning Algorithms for Solar Irradiance Forecasting in a Rural Community in Michoacan, Mexico
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Ana Martinez-Gamez, Heberto Ferreira-Medina, Bernardo Lopez-Sosa, Sayra Orozco, Mario Morales-Maximo, Carlos A. Garcia, Michel Rivero
Abstract - This project aims to develop a methodology for predicting solar radiation in San Francisco Pich´ataro, a community in the municipality of Tingambato, Michoac´an, Mexico. This community lies within the Pur´epecha indigenous zone. The project utilized two databases: one from a solarimetric station in the area and the other from the Solcast platform, which provides access to solar irradiance and other pertinent meteorological variables. Rigorous data cleansing and analysis procedures were implemented to ensure data quality and compatibility. Subsequently, both linear and decision tree regression models were applied to the refined and prepared data to forecast solar radiation.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Office Layout's Impact on Employee Productivity and Efficiency
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Haryadi Sarjono, Safina Alya Zahira, Ine Silviya, Boyke Setiawan Soertin
Abstract - This study aims to identify the office layout that best suits Gen Z workers' preferences and enhances productivity and work quality. A qualitative method with a descriptive approach was employed, focusing on Gen Z employees in the Information and Technology Division. Among the 38 employees in this division, ten are Gen Z, and eight of them participated in the study through a questionnaire and partial interviews to delve deeper into their responses. The questionnaire covered six different office layout types and assessed their impact on work productivity and efficiency. Gen Z employees in the Information and Technology Division favored new layouts, particularly the Relax Corner, Desk Facing Outside Window, Mini Bar, and WFO Feel Like WFC. They prefer cozy, flexible office spaces with diverse work environments. The findings suggest that these new office layouts can enhance productivity and work efficiency for Gen Z employees. However, some participants noted that their productivity and efficiency were more influenced by factors like their colleagues and teamwork rather than the office layout itself.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Performance analysis of Floodlight, ONOS, OpenDaylight and Ryu controllers in software-defined network
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Rolph Abraham YAO, Ferdinand Tonguim GUINKO
Abstract - Software-defined networking (SDN) is a growing concept that allows the separation of the control layer from the data layer, making the network programmable, and having a centralized view and management of the network. The control layer is an important component of the network because it is composed of controllers that play a role in supervising and controlling the entire SDN network. For efficient traffic management in SDN, it is essential to have a high-performance controller. In this paper, a performance analysis of Floodlight, ONOS, OpenDaylight (ODL) and Ryu controllers is analyzed. A custom network topology is created with Mininet. The ping and iperf tools are also used to evaluate the four controllers based on bandwidth utilization, jitter, packet transmission rate, round-trip time (rtt), and throughput. Our analysis reveals that in terms of jitter, bandwidth utilization, and throughput, ONOS has the best performance. Floodlight has better performance in terms of round-trip time (rtt) and ODL provides better performance in terms of transmission rate.
Paper Presenters
avatar for Rolph Abraham YAO

Rolph Abraham YAO

Burkina Faso
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

BRIDGING DIGITAL DIVIDE: A STUDY ON THE UMANG PLATFORM AND ITS IMPACT ON E-GOVERNANCE
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Swastika Das
Abstract - This paper discusses the role of the UMANG platform in achieving the goal of addressing the digital divide in Indian e-governance. The UMANG platform aggregates nearly 2,000 services across sectors--health, education, and finance--onto a singular mobile-first platform for which it strives to make accessible, transparent, and efficient. Under the Digital India initiative, the UMANG platform was launched in 2017. Despite rapid digitalization in India, especially in cities, its rural pockets lag significantly in terms of internet usage penetration, marking only 37.3% in rural areas, respectively. The present research looks into how the same platform is trying to reduce that gap by providing services in 22 Indian languages, Assisted Mode for those without proper digital literacy, and real-time updates in the furtherance of tracking services. Citizen engagement in the right direction, UMANG has streamlined interactions, minimised bureaucratic delays, and created transparency. Problems, however, are still seated there-like limited digital literacy, security of data, and resistance from some government departments. Finally, the study concludes that with continuous integration enhancements in digital security and wider citizen participation, UMANG can transform governance in India, paving the way towards realising the vision of Digital India.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

FlexiMind: Dyslexia Assessment and Aid Application for Specific Learning Disorders
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - D. I. De Silva, S.V. Sangkavi, W. M. K. H. Wije-sundara, L. G. A. T. D. Wijerathne, L. H. Jayawardhane
Abstract - This study introduces FlexiMind, an innovative mobile application designed to support children aged 6–10 with specific learning disorders, including dyslexia, dysgraphia, and dyscalculia. By integrating evidence-based instructional strategies and leveraging modern technologies, the application delivers an inclusive and interactive learning environment. The app comprises four core modules: Dyslexia Assessment, Tamil Letter Learning, Math Hands, and Word Recognition & Sentence Construction. These modules employ multisensory approaches, including real-time feedback, gesture-based learning, and machine learning algorithms, to enhance cognitive, linguistic, and mathematical skills. Preliminary findings highlight significant improvements in handwriting accuracy, letter recognition, phonemic awareness, and mathematical comprehension among children using FlexiMind. With its focus on Tamil language support and an adaptive design, FlexiMind addresses the unique needs of Tamil-speaking children while offering scalable solutions for broader educational contexts. This study underscores the potential of technology-driven tools in transforming learning experiences for children with specific learning disorders.
Paper Presenters
avatar for S.V. Sangkavi

S.V. Sangkavi

Sri Lanka
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Investigating Behavioral Responses across Landslide Scenarios in Virtual Reality
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Arjun Mehra, Arti Devi, Ananya Sharma, Sahil Rana, Shivam Kumar, K V Uday, Varun Dutt
Abstract - Virtual reality holds enormous potential for disaster preparedness; yet, little is known about how varying landslide risk levels and environmental elements (day vs night) impact people's physiological and psychological responses to these simulated catastrophes. By utilizing behavioral measures (Euclidean distance around collision, number of collisions, and velocity around collision), this study closes this gap by investigating stress and cognitive responses. Eighty volunteers were divided into four groups at random, and each group was exposed to a distinct set of landslide probabilities under various conditions: low likelihood during the day, high probability during the day, and high probability at night. The findings indicate that perceived risk significantly increased behavioral measurements, independent of time of day. These results demonstrate VR's capacity to improve cognitive engagement and equip participants to handle the psychological difficulties that arise in actual crisis scenarios.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Multi-Level Hybrid Ensemble with Attention based meta-learner for Diabetic Retinopathy Prediction
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Akriti Agarwal, Harshvardhan Singh Gahlaut, Annie Jain, Shalini L
Abstract - Most common complication of Diabetic mellitus is Diabetic Retinopathy: It causes the lesion to occur upon the retina and affects vision if not diagnosed early it triggers blindness. Diabetic retinopathy should be treated by an early diagnosis to avoid irreversible loss of vision. In addition, the manual diagnosis by ophthalmologists is less efficient and can easily miss the smallest detail that, in some cases, may not be visible to naked human eyes compared to the computer-aided systems. This implies proposing an existing supervised learning strategy for detection of DR from retinal fundus images to a hybrid combination of both deep learning InceptionV3 and ResNet and a machine learning model, namely Random Forest and Support Vector Machine. The model architecture incorporates advanced neural networks fused with classifiers which is further tuned and added up with an attention mechanism ensuring robust and one of the most accurate classification model of DR and non-DR cases. The dataset comprises of 30,000 fundus images which is preprocessed and augmented to improve model performance, hence addressing class imbalance. Additionally, a front-end app with Grad-CAM analysis is developed to classify DR and Non-DR images and visualize where the model focuses during classification.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Semantic Segmentation of Buildings using Optical Satellite Images and Deep Learning
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Nadia Liz Quispe Siancas, Julian Llanto Verde, Wilder Nina Choquehuayta
Abstract - Semantic segmentation of buildings using optical satellite images and deep learning techniques is essential for urban planning and monitoring, especially in suburban areas. In this study, we focused on evaluating the performance of six deep learning models: DeepLabV3 MobileNetV3, DeepLabV3 ResNet50, FCN ResNet50, EfficientNet-B0, ResNet101, and UNET. The dataset was collected from the province of Mariscal C´aceres, specifically in the district of Juanju´ı, located in the department of San Mart´ın, situated in the northeast of Peru. Our analysis revealed varying levels of precision for each model: DeepLabV3 MobileNetV3 achieved 74.14%, DeepLabV3 ResNet50 reached 83.35%, FCN ResNet50 attained 83.56%, EfficientNet-B0 yielded 61.37%, ResNet 101 obtained 63.60%, and UNET demonstrated 74.54%. These results provide insights into the effectiveness of different deep learning architectures for semantic segmentation tasks in suburban environments.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

UAV image motion deblurring methods in precision agriculture: A Bibliometric Analysis to A Short Survey
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Ambroise D. K. Houedjissin, Arnaud Ahouandjinou, Manhougbe Probus A. F. KIKI, Francois Xavier Ametepe, Kokou M. Assogba
Abstract - Image motion deblurring is an important issue in computer vision applications which encounter challenges like motion blur caused by camera shake, fast motion or irregular deformation of agricultural living things during image acquisition. Images acquired by UAV-embedded cameras are often blurred and usually error-prone in precision agriculture. So, image deblurring in applications such as plant phenotyping recognition, crop pests and diseases detection or animal behavior analysis is a great challenge. The main purpose of this paper is to carry out both a bibliometric analysis to assess the current research trends on UAV image motion deblurring with a brief survey of the main image motion deblurring techniques in agriculture. So, we used the Scopus database and 2138 articles were retrieved. This dataset has then been analyzed using a bibliometric tool. According to results, the most impactful authors have 53 and 46 publications respectively. Remote Sensing is the most impactful journal with an h-index of 49 and 285 published articles whereas China is the country with the most impactful production and the most cited document, indicating its considerable influence in this area of research. Results from the short survey indicate that further research is needed to develop more robust and efficient motion deblurring techniques tailored to the specific challenges of UAV imagery in precision agriculture.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Analysis of Reconfigurable Frequency Selective Surface FSS Using Light-Dependent Resistor LDR
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Mariam Basim Al-Najjar, Khalil H. Sayidmarie
Abstract - This contribution investigates a proposed Frequency Selective Surfaces (FSS) that can be reconfigured using light-dependent resistor LDR. The unit cell of the FSS comprises a split square ring equipped with a single LDR placed at its gap. The FSS is built on the FR4 substrate of 40X40 mm dimensions, and ring size of 29 x29 mm to serve the WLAN application of 2.45 GHz frequency. When the LDR is adequately illuminated it exhibits a small resistance, and the ring behaves as a closed one, while in the dark condition, the resistance is high and the ring acts as a split ring. Therefore, the FSS works as a bandpass filter when illuminated, and as a bandstop filter without illumination. The LDR doesn’t need biasing wires that usually interfere with the structure of the FSS.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Data Search in Smart GIS Database using Map Reduce Pattern and Bayesian Probability
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Moubaric Kabore, Abdoulaye Sere, Vini Yves Bernadin Loyara
Abstract - This paper deals with Bayesian approach in Data Research in GIS database through artificial Intelligence (AI) modules, reading the best bayesian probability before returning the data requested, denoted AI4DB. The proposed method combines meshing techniques and the map-reduce algorithm with Bayesian approach to obtain a smart GIS database to reduce the execution time. According to the values of the Bayesian probability, the nearest sites of any position resulting of the user requests, are extracted speedily from the database using the map reduce framework. The execution time is less than the time for the case of the classical method, based only on a parallelism search without a probability. Only a map function with the best bayesian probability for the data in entry, executes entirely its instruction.
Paper Presenters
avatar for Abdoulaye Sere

Abdoulaye Sere

Burkina Faso
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Nature-based Solutions and Smart Cities
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Denis Vasiliev, Rodney Stevens, Lennart Bornmalm
Abstract - The idea of smart cities is becoming increasingly popular. Technology alone, however, is not sufficient for addressing the challenges that modern cities face. Pressures stemming from pollution, stress, and changing environmental conditions can ruin the lives of city dwellers, putting enormous pressure on public finance to address or mitigate consequences of the issues. Application of Nature-based Solutions could address multiple societal and environmental issues common to modern cities. Furthermore, the solutions can immensely benefit from integration with modern technologies. In fact, modern technologies can enhance the implementation, monitoring, and scalability of Nature-based Solutions. This makes a strong case for the application of Nature-based Solutions in modern urban environments that aspire to promote technology and become smart cities. Integration of the solutions into smart cities, however, is not a trivial task and requires a holistic approach to city planning and deep understanding of the ways to do so. Justification of the associated costs requires thorough understanding of the benefits, including the values that may be easily overlooked. Thus, in this study, we apply a conceptual research approach to explore how Nature-based Solutions can be integrated into Smart Cities, what are key benefits of the approach, and how this integration can address significant challenges in urban environments.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Semi-supervised learning based image sequence segmentation using recurrent autoencoder
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Victor Sineglazov, Andrew Sheruda
Abstract - In this paper, a new hybrid segmentation method based on SSL (semi-supervised learning) was developed for samples with image sequences, not all of which were labeled. Thus, this method can find application in areas where labeling is expensive or requires a certain specialist, such as in medicine. The developed method was evaluated on a sample of echocardiography images of patients with infective endocarditis in the context of a real-world task of segmenting heart valve anomalies. As a result, the accuracy gain compared to supervised learning is 5% in the IOU metric, while with other SSL methods it is on average 3%.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

SHair: A Web-based System for Hair Donation to Cancer Patients
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Ervin Mikhail G. Garcia, Zara Naomi S. Inocencio, Ronnel Christian B. Langit, Paul James L. Perez, Harold Russell P. Visperas, Mary Jane C. Samonte
Abstract - SHair is an innovative and very useful web-based tool that is made to make the process of donating hair easier. Its primary objective is to aid cancer patients in regaining self-confidence. SHair empowers hair donation by providing a user-friendly site that is safe for anybody wishing to give their hair. The main goal of the website is to have a substantial impact on the well-being of cancer patients by facilitating the connection between persons who express the intention to contribute their hair and those who need facial hair transplants. Individuals must possess this quality to foster a comprehensive comprehension of one another and collectives must possess it to increase their resilience. People stress how easy hair renewal is because it can bring donors and patients together, which is good for patients' mental health. SHair also makes sure that the processing and distribution of given hair can happen, which helps with efforts that focus on the happiness and well-being of cancer patients.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

The Potential Role of Generative Artificial Intelligence in Fostering a Holistic Approach to Nature-based Solution Implementation
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Denis Vasiliev, Rodney Stevens, Lennart Bornmalm
Abstract - Implementation of Nature-based Solutions is becoming increasingly widespread. The solutions are intended to simultaneously address environmental, social and economic challenges. This approach is intended to foster sustainable development. However, mere use of nature for addressing specific problems does not necessarily result in simultaneous delivery of value in all three sustainability areas. To make sure that Nature-based Solutions serve both nature and society and deliver maximal benefits, a holistic approach to their implementation is essential. Implementing this approach is, however, not a trivial goal. It requires joint consideration of environmental, social and economic factors at a range of spatial and temporal scales. Furthermore, collaboration among multiple diverse stakeholders in the context of rapidly changing systems is essential. This often involves processing large volumes of data and can be very labor intensive. As a result, the costs of such projects may be overly high, hindering their implementation. The emerging technology of Generative Artificial Intelligence can greatly facilitate the process, bringing down the costs and increasing speed and feasibility of the project implementation. However, lack of awareness and understanding of how such tools can be used in Nature-based Solutions projects may result in missing these opportunities. Thus, this paper explores potential applications of Generative Artificial Intelligence tools in pro-jects involving Nature-based Solutions.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Deterministic Framework for Ethical AI in Automated Lending Services: Addressing Risk, Governance, and Equity
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Vikas Shah, Travis Rice, Aarav Shah, Aarush Shah
Abstract - Artificial intelligence (AI) empowered the transformation of decision-making processes for lending services, delivering improved efficiency, scalability, and precision. However, the adoption of AI in loan origination and application processing has introduced significant ethical challenges, including recognizing biases, fairness, transparency, reliability, and accountability. The paper identifies primary challenges in automated lending services (ALS), AI-enabled decision-making, and the deriving of AI governance practices. This paper proposes a deterministic framework (DF) designed to systematically identify and address the ethical dimensions of AI in lending services. The DF spans comprehensive mechanisms encompassing data collection, preprocessing, model development, deployment, monitoring, and governance. Core ethical dimensions of explainability, transparency, and equitable outcomes are recognized within the governance lifecycle stages. The DF continuously integrates novel industry regulatory standards and governance methodologies to identify, measure, and mitigate ethical risks, ensuring operational efficiency and adherence to ethical principles. This research provides a structured approach grounded in deterministic methods, enabling measurable, repeatable, and auditable business processes to enable trust and accountability in AI-driven ALS. An empirical case study focusing on ALS for students and their families is presented to evaluate the DF's applicability and effectiveness. The findings provide actionable insights for financial institutions, policymakers, and technologists seeking to implement ethical AI practices, strengthen risk management, and deliver equitable and accountable lending services to diverse populations.
Paper Presenters
avatar for Vikas Shah

Vikas Shah

United States of America
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

IMOK: A compact connector for non-prohibition proofs to privacy-preserving applications
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Oleksandr Kurbatov, Lasha Antadze, Ameen Soleimani, Kyrylo Riabov, Artem Sdobnov
Abstract - This article proposes an extension for privacy-preserving applications to introduce sanctions or prohibition lists. When initiating a particular action, the user can prove, in addition to the application logic, that they are not part of the sanctions lists (one or more) without compromising sensitive data. We will show how this solution can be integrated into applications, using the example of extending Freedom Tool (a voting solution based on biometric passports). We will also consider ways to manage these lists, versioning principles, configuring the filter data set, combining different lists, and using the described method in other privacy-preserving applications.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Systematic Mapping Study of Wireless Communication Technologies in In-Vehicle Network
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Asmaa Berdigh, Kenza Oufaska, Khalid El Yassini
Abstract - The inclusion of wireless communication technologies in the car industry has drastically reshaped it over the last two decades. This evolution began with the integration of Bluetooth and Smart Key technology in 1998 and has since progressed to the application of wireless communication in the cell monitoring controller of battery management systems. The objective of this study is to conduct an SMS to evaluate the research investigating wireless communication technologies used in the in-vehicle network. We adopt the Systematic Mapping Study (SMS) approach, a rigorously defined research methodology with roots in the medical and software engineering, using defined criteria to filter out the research contributions stored in both Scopus and GoogleScholar databases over the last twenty years. We synthesize the resulting data and produce this article. This work aims to organize information from studies published within this disciplinary field over the past two decades, presenting them in a systematic map form, and discussing the results and their implications for future research, concluding by a visual display of which automotive domains have the largest wireless communication. The SMS provides a clear and comprehensive picture drawn from precise questions. The derived outcomes could hold significance for both researchers and industry professionals considering the integration of wireless communication technologies within in-vehicle networks. Despite the substantial body of research identified over the past 20 years on wireless technologies, only a limited number of studies have specifically focused on the In-Vehicle Networks.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Towards a Cybersecurity Culture Framework: A Literature Review of Awareness and Behavioral Transformation in Telecommunications Organizations
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Esther Endjala, Hanifa Abdullah, Mathias Mujinga
Abstract - This paper explores the theoretical and strategic foundations for cultivating a cybersecurity culture within telecommunications institutions. Drawing on established behavioral theories Social Cognitive Theory (SCT), Protection Motivation Theory (PMT), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM) it examines the opportunities for enhancing cybersecurity awareness and transforming employee behaviors into a resilient human firewall. The paper synthesizes existing literature to highlight the role of leadership, employee engagement, training, collaboration, and recognition in fostering a cybersecurity culture. The review further identifies gaps and limitations in the current approaches, proposing a conceptual foundation for developing an effective cybersecurity culture framework tailored to telecommunications institutions. Appropriate cybersecurity culture is essential in developing the entity and helps protect organizational assets such as data, networks, and systems when technical defenses are quite significant. The section takes into consideration the theoretical aspect of cybersecurity culture and comes out with a derived underlying framework that incorporates aspects like the Social Cognitive Theory, Protection Motivation Theory, Theory of Planned Behavior, and Technology Acceptance Model. Creating a strong cybersecurity culture faces several significant challenges, including resistance to change, limited resources, and regulatory hurdles. The proposed framework emphasizes the importance of top management commitment, employee involvement, ongoing training, and interdepartmental collaboration as vital components for cultivating this culture. Organizations should address these challenges to successfully establish an effective cybersecurity culture. By integrating these elements, cybersecurity can become a fundamental organizational value, enhancing awareness, compliance, and employee engagement. This paper establishes the groundwork for further research on cybersecurity culture and outlines key steps to strengthen organizational resilience and ensure a safe digital environment.
Paper Presenters
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Train-the-Trainer: Empowering Educators with Practical AI and Robotics Skills through the CDIO Framework
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Authors - Amna Altaf, Zeashan Khan, Adeel Mehmood, Jamshed Iqbal
Abstract - Advancements in Artificial intelligence (AI) and robotics call for prioritising research in education and pedagogy in these domains including teachers’ training to practice emerging learning and teaching strategies. This paper explores the model of teachers’ training by using CDIO (Conceiving, Designing, Implementing and Operation) framework as a guide and a robotic platform as an example in AI education. In the pilot study, nine workshop sessions were designed and organised for a group of five teachers introducing them to robotics-led delivery of AI content using a mobile robot ‘Duckiebot’. The prominent workshop contents include robot vision, object detection, state estimation and localisation, task planning and reinforcement learning. Preliminary results in the form of feedback from the workshop participants demonstrated that the teaching model presented in this study made a promising contribution in terms of improving teachers’ intellectual and pedagogical skills as well as their confidence in achieving learning outcomes. The presented CDI-based robotics-led AI teaching model adds to the dialogue on innovative AI and engineering education methods. It is anticipated that wider dissemination of the findings in this research will lay the groundwork for a larger educational impact.
Paper Presenters
avatar for Amna Altaf

Amna Altaf

United Kingdom
Thursday February 20, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

3:30pm GMT

Session Chair Remarks
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Virtual Room A London, United Kingdom

3:30pm GMT

Session Chair Remarks
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Virtual Room B London, United Kingdom

3:30pm GMT

Session Chair Remarks
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Virtual Room C London, United Kingdom

3:30pm GMT

Session Chair Remarks
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Thursday February 20, 2025 3:30pm - 3:33pm GMT
Virtual Room D London, United Kingdom

3:33pm GMT

Closing Remarks
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Virtual Room A London, United Kingdom

3:33pm GMT

Closing Remarks
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Virtual Room B London, United Kingdom

3:33pm GMT

Closing Remarks
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Virtual Room C London, United Kingdom

3:33pm GMT

Closing Remarks
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Thursday February 20, 2025 3:33pm - 3:35pm GMT
Virtual Room D London, United Kingdom

4:13pm GMT

Opening Remarks
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Virtual Room A London, United Kingdom

4:13pm GMT

Opening Remarks
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Virtual Room B London, United Kingdom

4:13pm GMT

Opening Remarks
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Virtual Room C London, United Kingdom

4:13pm GMT

Opening Remarks
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Thursday February 20, 2025 4:13pm - 4:15pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Exploring and Visualizing COVID-19 Trends in India: Vulnerabilities and Mitigation Strategies
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Swayamjit Saha, Garga Chatterjee, Kuntal Ghosh
Abstract - Visualizing data plays a pivotal role in portraying important scientific information. Hence, visualization techniques aid in displaying relevant graphical interpretations from the varied structures of data, which is found otherwise. In this paper, we explore the COVID-19 pandemic influence trends in the subcontinent of India in the context of how far the infection rate spiked in the year 2021 and how the public health division of the country India has helped to curb the spread of the novel virus by installing vaccination centers and administering vaccine doses to the population across the diaspora of the country. The paper contributes to the empirical study of understanding the impact caused by the novel virus to the country by doing extensive explanatory data analysis of the data collected from MoHFW, India. Our work contributes to the understanding that data visualization is prime in understanding public health problems and beyond and taking necessary measures to curb the existing pandemic.
Paper Presenters
avatar for Swayamjit Saha

Swayamjit Saha

United States of America
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Feedback-Matching Neural Network for Time Series Forecasting
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Louay Al Nuaimy, Hazem Migdady, Mahammad Mastan
Abstract - Accurate time series forecasting is vital in areas such as finance, weather prediction, and energy management. Traditional forecasting methods often struggle to effectively model the intricate patterns and nonlinearities present in real-world data. This study proposes the feedback-matching neural network (FMNN), a deep learning model that evolves from the feedback-matching algorithm (FMA). By embedding the core concepts of FMA into a neural network structure, the FMNN can recognize and match historical patterns in time series data, leading to more accurate predictions. Extensive experiments reveal that the FMNN outperforms several conventional statistical models and modern neural networks in terms of forecasting accuracy, as evaluated by the weighted absolute percentage error (WAPE). The FMNN enhances prediction accuracy by offering a sophisticated method for identifying and leveraging repeating trends within the data.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

MYv7: New 3D Monocular Object Detection Improvement for Road and Railway Smart Mobility
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Alexandre Evain, Redouane Khemmar, Mathieu Orzalesi, Sofiane Ahmedali
Abstract - This paper presents MYv7 (Mono-YOLOv7), an adaptation of the YOLOv7 architecture tailored specifically for 3D monocular object detection. Rather than competing with specialized 3D methods, we demonstrate the efficacy of enhancing 3D monocular detection using improved 2D object detection algorithms. We showcase how improvements in 2D algorithms can enhance 3D predictions, presenting MYv7’s twofold advantage over a YOLOv5-based method: increased speed and accuracy. These gains are crucial for efficient operation on embedded systems with limited computational resources. Our results highlight the potential of using advancements in 2D detection methods to significantly improve 3D monocular object recognition, opening new avenues for real-world applications.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Optimizing V2X Communications for 6G: A Summary of Techniques and AI Methods
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Ali Belgacem, Abbas BRADAI
Abstract - This summary research paper provides a comprehensive overview of Vehicle-to-Everything (V2X) communications, including various communication types and the roles of base stations. It covers resource allocation techniques and beamforming for high-quality connectivity and addresses energy efficiency optimization metrics. The paper also discusses artificial intelligence methods and their integration to optimize these systems and enhance performance. This research serves as a valuable guide for those aiming to contribute to advancements in 6G technologies for efficient vehicular communications.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Parallel fitness scores evaluation to improve training speed of the NEAT algorithm using GO routines
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Iaroslav Omelianenko
Abstract - Neuroevolution algorithms need to evaluate at the end of each epoch the fitness scores of each organism in a population of solvers within the problem space where a solution is sought. This evaluation often involves running complex environmental simulations, which can significantly slow down the training speed if done sequentially. This work proposes a solution that utilizes the inherent capabilities of the Go programming language to run complex simulations in local parallel processes (routines). The efficiency of this proposed solution is compared to sequential evaluation using two classic reinforcement learning experiments, specifically single and double pole balancing. Direct comparisons indicate that the proposed solution is up to five times faster than the sequential approach when complex environmental simulations are required for objective function evaluation.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Strategies for Culturally Responsive AI in Education: Mitigating Bias and Enhancing Student Outcomes
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Kayode Oyetade, Anneke Harmse, Tranos Zuva
Abstract - The introduction of AI in education has the potential to address educational inequalities and improve outcomes, but it also raises concerns about cultural responsiveness and biases in AI systems. To ensure equitable outcomes, strategies are needed to address these concerns. However, there is a limited understanding of effective approaches for promoting cultural sensitivity and equity in AI-powered educational content, highlighting a significant gap in existing literature. Using literature review methodology, this study aims to explore strategies to enhance cultural sensitivity and mitigate biases in AI-powered educational content, focusing on the intersection of technology and cultural diversity. By addressing concerns related to bias in AI algorithms, our findings highlight the importance of cultural inclusivity in AI-driven educational tools and advocates for proactive measures to embed cultural responsiveness into AI development processes. This review contributes to the discussion on responsibly integrating AI in education, promoting educational environments that value and reflect diverse cultural identities, and promoting a more inclusive educational experience globally.
Paper Presenters
avatar for Kayode Oyetade

Kayode Oyetade

South Africa
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Automated identification and analysis of Barret’s Taxonomy levels in reading comprehension assessment tasks: A GPT-based approach
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Eduardo Puraivan, Patricio Tapia, Miguel Rodriguez, Steffanie Kloss, Connie Cofre-Morales, Pablo Ormeno-Arriagada, Karina Huencho-Iturra
Abstract - This study provides empirical evidence on the effectiveness of large language models (LLMs), particularly ChatGPT, for automating the identification and analysis of cognitive demand levels in reading comprehension assessment tasks, using Barret’s Taxonomy. The manual classification of these tasks, even for experienced teachers, poses challenges due to their complexity and the time required. To address this issue, a three-step methodology was developed: selection of reading comprehension activities, automatic classification by ChatGPT, and comparison with the classifications from a group of experts. The experiment included 25 questions based on four readings extracted from a fourth-grade teacher’s guide for primary education. The results showed variability in the agreement between ChatGPT’s classifications and those of the experts: 77% in Activity 1, 50% in Activity 2, 52% in Activity 3, and 67% in Activity 4. At the question level, agreements ranged from 0% to 100%, highlighting discrepancies even among the evaluators, which underscores the inherent subjectivity of the task. Despite these divergences, the results emphasize the potential of LLMs to streamline the classification of educational activities on a large scale and the need to continue refining these models to enhance their performance in more complex pedagogical tasks.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Comparison of Machine Learning Algorithms in Water Quality Index Prediction: A Case Study in Juiz de Fora, Brazil
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Larissa de Lima, Priscila Capriles, Nathan Oliveira
Abstract - This paper explores the use of machine learning (ML) with various physical, chemical, and biological parameter combinations to predict water quality, focusing on the Water Quality Index (WQI). We assess the performance of several regression algorithms across five different data combinations and examine the impact of inference and class balancing techniques on model outcomes. Our analysis reveals that LightGBM achieved the highest accuracy in WQI regression at 93%. This research introduces a novel approach to calculatingWQI by automating the traditional manual and complex parameter collection and calculation process. By streamlining water quality monitoring, our ML-based method offers a more efficient and innovative solution. Additionally, the study provides practical insights into handling data scarcity and using statistical inference for skewed sampling distributions.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Design and Implementation of a Secure and Efficient Blockchain-Based Investment Platform with PBFT Consensus
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Atiqur Rehman, Karim Elia Fraoua, Amos David
Abstract - Blockchain technology has the potential to revolutionize traditional financial systems by offering decentralized, secure, and transparent transaction processing. This research focuses on developing a blockchain-based investment platform that integrates the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The platform addresses critical issues faced by traditional investment systems, such as security vulnerabilities, inefficiencies, and the lack of transparency. By incorporating smart contracts, the platform automates key investment processes such as order placement and settlement, significantly reducing reliance on intermediaries. The system is designed to process transactions in real-time, offering high throughput and low latency, ensuring a smooth user experience. Extensive testing, including unit testing, integration testing, and security testing, has been conducted to verify the platform’s performance, scalability, and robustness. Security measures such as end-to-end encryption and multi-factor authentication (MFA) further enhance the platform's reliability. While PBFT ensures fast and secure consensus, the scalability of the algorithm may present challenges as the platform grows. Future work will focus on optimizing the PBFT system, exploring hybrid blockchain models, and integrating the platform with external financial systems to extend its applicability. The research demonstrates that blockchain, when combined with PBFT, can create a secure, efficient, and scalable solution for managing investment transactions.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Extending XReason: Formal Explanations for Adversarial Detection
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Amira Jemaa, Adnan Rashid, Sofiene Tahar
Abstract - Explainable Artificial Intelligence (XAI) plays an important role in improving the transparency and reliability of complex machine learning models, especially in critical domains such as cybersecurity. Despite the prevalence of heuristic interpretation methods such as SHAP and LIME, these techniques often lack formal guarantees and may produce inconsistent local explanations. To fulfill this need, few tools have emerged that use formal methods to provide formal explanations. Among these, XReason uses a SAT solver to generate formal instance-level explanation for XGBoost models. In this paper, we extend the XReason tool to support LightGBM models as well as class-level explanations. Additionally, we implement a mechanism to generate and detect adversarial examples in XReason. We evaluate the efficiency and accuracy of our approach on the CICIDS-2017 dataset, a widely used benchmark for detecting network attacks.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Insurance Fraud Detection using Machine Learning
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Ayush Verma, Krisha Patel, Hardikkumar Jayswal, Nilesh Dubey, Dipika Damodar
Abstract - Insurance fraud significantly undermines the financial stability of the insurance industry, resulting in billions of dollars lost annually due to fraudulent claims across sectors like healthcare, auto, and property insurance. This paper proposes a robust methodology for detecting insurance fraud through the strategic implementation of ensemble machine learning algorithms, specifically XGBoost and Random Forest. By analyzing extensive datasets that include policyholder demographics, claim histories, and risk factors, we develop predictive models that accurately identify fraudulent activities while minimizing false positives. The effectiveness of our approach is supported by a comprehensive literature review highlighting the performance of various machine learning models in fraud detection, as well as our application of preprocessing techniques and feature selection to enhance model accuracy. Our findings indicate that the integration of advanced AI and ML technologies can revolutionize fraud detection in the insurance sector, offering a more secure and efficient environment for both insurers and policyholders.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Modeling and simulation of mushroom cultivation in a protected environment using Fuzzy Logic
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Honorato Ccalli Pacco
Abstract - Mushrooms are important in human nutrition due to their nutritional value in terms of protein, vitamin and mineral content. The volume of mushroom cultivation is currently increasing. This research focuses in the modeling and simulation of temperature, humidity and irrigation time controlling in mushroom cultivation in a protected environment. Using fuzzy logic in an intelligent system that allows process control and the LabVIEW software that facilitates graphic programming by means of virtual instruments, the irrigation time program was obtained as an output variable or an input variable-dependent response (input variables were temperature and humidity) in the intelligent system. The result was a program that shows how to act in different situations of temperature and humidity in mushroom cultivation in a protected environment. The fuzzy logic program in LabVIEW allowed the simulation of the system in terms of irrigation time in mushroom cultivation in a protected environment to achieve the expected results. In experimental results it can be observed that at low temperatures (15 °C) and low humidity (35%) the irrigation time is an average value (44.03). With the high temperature (35°C) and high humidity (95%) in the protected environment, the irrigation time will be with a low value (22.32). And it could be simulated by varying the input variables.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Artificial Intelligence (AI)-Supported Gamification for Learning Performance: Insight into Advancing Learning Intrinsic Motivation
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Graphene for Qubits: A Brief Review
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Md Asif Ahmed, Md Sadatuzzaman Saagoto, Farhan Mahbub, Protik Barua
Abstract - Graphene is emerging as a strong candidate for qubit applications in quantum computing due to its unique properties and recent technological advancements. Graphene, as a two-dimensional material with high carrier mobility and distinct electron behavior, presents potential advantages for qubit applications. However, its zero-band-gap nature poses challenges for stable quantum states, requiring innovative solutions to realize its full potential in quantum computing. This review explores graphene's unique properties and their impact on qubit design, analyzing recent breakthroughs aimed at overcoming its inherent limitations, such as techniques for band-gap modulation and substrate engineering. We delve into various methodologies, including the integration of hexagonal boron nitride (hBN) and electrostatic gating, to enhance graphene's performance for quantum applications. Additionally, we examine the integration of graphene with other 2D materials and hybrid structures to achieve tunable quantum properties, essential for advancing scalable quantum architectures. This comprehensive analysis aims to bridge the material science challenges with the practical demands of qubit technology, providing a roadmap for leveraging graphene in future quantum systems.
Paper Presenters
avatar for Farhan Mahbub

Farhan Mahbub

Bangladesh
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Security implementations during the development of enterprise mobile applications: lessons learnt
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Kobus Kemp, Lynette Drevin, Magda Huisman
Abstract - This paper reports on a study that explores and addresses security challenges in the development of enterprise mobile applications (EMAs). Despite the growing prevalence of mobile applications, security considerations are often overlooked or insufficiently addressed in mobile application software development methodologies. This gap highlights the need to incorporate security training into software developer education. The study used a literature review of software development methodologies (SDMs) and security practices, complemented by case studies involving interviews with industry experts on EMA development processes. Using thematic and cross-case analyses, the study produced a framework designed to guide the integration of security measures into EMA development. Findings revealed a limited emphasis on security aspects in current mobile application development practices. Consequently, a partial framework is presented in this paper, detailing key security considerations and countermeasures specific to EMA development. This research contributes to the discipline by offering developers guidelines to enhance security in EMAs, emphasizing the importance of integrating these practices into developer training programs.
Paper Presenters
avatar for Kobus Kemp

Kobus Kemp

South Africa
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Squeezing Hidden Knowledge from Scarce Data: A Technique Tested on Limited Data of a Language Pathology
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Marisol Roldan-Palacios, Aurelio Lopez-Lopez
Abstract - Limited available data becomes a critical problem in specific machine learning tasks, where approaches, such as large language models, turn impractical. Reaching solutions in such situations requires alternative methods, especially whether the object of study contributes to data scarcity while preventing using techniques such as data augmentation. This scenario led us to formulate the research question on how to squeeze hidden information from small data. In this work, we propose a data processing and evaluation technique to increase information extraction from scarce data. Attributes expressed as trajectories are further pair-related by proximity and assessed by customary learning algorithms. The efficacy of the proposed approach is tested and validated in language samples from individuals affected by a brain injury. Direct classification on raw and normalized data from three sets of lexical attributes works as a baseline. Here, we report two learning algorithms out of five explored, showing consistent behavior and demonstrating satisfactory discriminatory capabilities of the approach in most cases, with encouraging percentages of improvement in terms of f1-measure. We are in the process of testing the approach in language data sets of syntactic and fluency features, but other fields can take advantage of the technique.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Technology Adoption for Advancing Learning Quality Performance: Insights into Technology-Assisted Instructional Design
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

User Data Privacy on Social Media: Policies, Practices, and Perceptions
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Khalaf Elwadya, Khosro Salmani
Abstract - The evolution of social media platforms has led to the creation of a dynamic ecosystem, abundant in user-generated content. This, however, has also resulted in raising concerns about data privacy. Beyond potential threats like scammers exploiting freely shared information on social media for spying, financial scams, social media companies can leverage user data to sell targeted advertising. Addressing these issues necessitates heightened user awareness. Hence, this paper first examines the privacy policies of major social media platforms including TikTok, Twitter, Facebook, Instagram, and LinkedIn, providing a comparative analysis of their data storage practices, utilization of user information, account verification requirements, and default privacy settings. Next, we undertake an extensive survey utilizing the data gathered in the initial phase to evaluate user awareness regarding the utilization of their data, highlighting a notable gap between policy stipulations and user expectations. We conclude with four recommendations based on our findings to help social media companies refine their privacy policies, promoting more comprehensible guidelines.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Advancing Point Cloud Classification with Deep Learning by Optimizing PointNet through Transformations for Superior Accuracy
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Muhammad Sufyan Akbar, Guo Jiandong, Muhammad Irfan Khan, Asif Iqbal, Salim
Abstract - This paper introduces a deep learning-based approach for point cloud classification, leveraging the PointNet architecture to optimize 3D object recognition. The method effectively addresses the challenges associated with unordered point cloud data, achieving superior classification performance with 92% accuracy, 91% precision and recall, 89% F1-score, and 96% sensitivity and specificity. The proposed model captures spatial features directly from raw point cloud data, demonstrating its potential for real-world applications in 3D object recognition and scene understanding. Comprehensive experiments on benchmark datasets validate the model’s effectiveness in classifying complex 3D structures, highlighting its robustness and efficiency. Future research will focus on advancing feature extraction techniques and refining the model to enhance classification performance under more demanding conditions.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

AI-Enhanced Instruction Design: Insights into Constructive Learning Support
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors -
Abstract -
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Data-Driven Analysis of Women Unemployment in Sub Saharan Africa: A Multiple Correspondence Approach for Promoting Sustainable Development Goal
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Timothy T Adeliyi, Funmi Adebesin, Edidiong R Umoh
Abstract - Unemployment remains a persistent challenge for both developed and developing countries, leading to the underutilisation of resources. Many Sub-Saharan countries experience high unemployment rates due to weak economic indicators. This study adopts a data-driven approach to investigate women's unemployment in Sub-Saharan Africa, with a focus on the factors contributing to employment disparities and advancing gender equality, for Sustainable Development Goal 5 (SDG 5). Using Multiple Correspondence Analysis (MCA), the research identifies and analyses key factors that contributes to the high unemployment rates among women in the region. The findings reveal significant links between unemployment and factors such as age, region, and wealth index. By shedding light on these disparities, the study offers a comprehensive understanding of the structural barriers faced by women in the labour market. The results emphasise the need for specific policies and interventions to combat gender inequality and boost women's economic participation to achieve SDG 5. This research enriches the broader dialogue on sustainable development and gender equality, providing crucial insights for policymakers and stakeholders working towards more inclusive labour markets in Sub-Saharan Africa.
Paper Presenters
avatar for Timothy T Adeliyi

Timothy T Adeliyi

South Africa
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Enhancing Ovarian Tumor Diagnosis Through Transfer Learning in Convolutional Neural Networks
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Salimah Saeid, Tahani Almabruk, Muetaz Abdulsamad
Abstract - The diagnosis of ovarian tumors remains a challenging task due to the inherent variability and complexity of imaging data. This study evaluates the efficacy of transfer learning and fine-tuning techniques in convolutional neural net-works to enhance the classification accuracy of ovarian tumors in ultrasound images. The performance of YOLOv8 and VGG16 models were compared, including a modified VGG16 architecture optimized for this application. YOLOv8 models were evaluated both from scratch and with pre-trained weights, while VGG16 was employed for feature extraction and fine-tuning. The Modified VGG16 outperformed all other models, achieving the highest classification accuracy (%90) and the shortest training time (8.63 hours). Advanced data augmentation strategies and architectural optimizations effectively addressed issues such as class imbalance and overfitting. These results highlight the potential of customized CNN architectures and transfer learning to improve diagnostic accuracy and efficiency, advancing the development of reliable tools for ovarian tumor classification in clinical imaging.
Paper Presenters
avatar for Salimah Saeid

Salimah Saeid

Libyan Arab Jamahiriya
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Predicting Lithium-Ion Battery State of Health with Hybrid Ensemble Modeling
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Mohammad Anwar Rahman, Rafiul Hassan
Abstract - Accurate prediction of lithiumion batteries' state of health (SOH) is crucial for preventing catastrophic system failures. This study investigates the application of ensemble modeling to characterize capacity degradation and fore-cast remaining charge-discharge cycles. Leveraging NASA's battery charge/discharge dataset, we developed and compared feed-forward neural network (FNN) and random forest (RF) regression models. To enhance predictive accuracy, we constructed an ensemble model that combines the strengths of both individual models. A key aspect of our methodology was the accurate evaluation of model performance across different battery datasets. Rather than using a single dataset for training and testing, we adopted a cross-validation approach to assess model generalization capabilities. This strategy allowed us to identify the robustness of the models for predicting SOH and estimating remaining battery life. Our findings indicate comparable performance among the FNN, RF, and ensemble models. While all models demonstrated effective capacity degradation prediction, the ensemble model exhibited slightly superior performance in a few scenarios. These findings emphasize the advantages of ensemble modeling in enhancing the accuracy and reliability of lithiumion battery prognostics.
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Remote Voting System Using Biometric Authentication
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Authors - Dattatraya Adane, Lakshya Agrawal, Shradha Wangota, Sharvari Inamdar
Abstract - In order to overcome issues with traditional voting, such as voter impersonation, booth capturing, and logistical inefficiencies, this study proposes a biometric voting method. The technology guarantees that only authorised individuals can cast ballots while maintaining anonymity by combining fingerprint authentication with secure digital platforms. It features a web-based interface for election managers to manage candidates, track votes, and display real-time results, as well as a mobile app for voter registration and remote voting, improving accessibility. The solution lowers costs while increasing security, transparency, and engagement by utilising technologies like Firebase Firestore, the Mantra MFS100 fingerprint scanner, and QR code integration. This essay examines its design, use, and effects on modernising elections to promote efficiency, inclusivity, and trust.
Paper Presenters
Thursday February 20, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

5:45pm GMT

Session Chair Remarks
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Virtual Room A London, United Kingdom

5:45pm GMT

Session Chair Remarks
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Virtual Room B London, United Kingdom

5:45pm GMT

Session Chair Remarks
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Virtual Room C London, United Kingdom

5:45pm GMT

Session Chair Remarks
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Thursday February 20, 2025 5:45pm - 5:47pm GMT
Virtual Room D London, United Kingdom

5:47pm GMT

Closing Remarks
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Virtual Room A London, United Kingdom

5:47pm GMT

Closing Remarks
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Virtual Room B London, United Kingdom

5:47pm GMT

Closing Remarks
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Virtual Room C London, United Kingdom

5:47pm GMT

Closing Remarks
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Thursday February 20, 2025 5:47pm - 5:50pm GMT
Virtual Room D London, United Kingdom
 
Friday, February 21
 

9:28am GMT

Opening Remarks
Friday February 21, 2025 9:28am - 9:30am GMT
Friday February 21, 2025 9:28am - 9:30am GMT
Virtual Room A London, United Kingdom

9:28am GMT

Opening Remarks
Friday February 21, 2025 9:28am - 9:30am GMT
Friday February 21, 2025 9:28am - 9:30am GMT
Virtual Room B London, United Kingdom

9:28am GMT

Opening Remarks
Friday February 21, 2025 9:28am - 9:30am GMT
Friday February 21, 2025 9:28am - 9:30am GMT
Virtual Room C London, United Kingdom

9:28am GMT

Opening Remarks
Friday February 21, 2025 9:28am - 9:30am GMT
Friday February 21, 2025 9:28am - 9:30am GMT
Virtual Room D London, United Kingdom

9:28am GMT

Opening Remarks
Friday February 21, 2025 9:28am - 9:30am GMT
Friday February 21, 2025 9:28am - 9:30am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Adoption Intentions of Mobility-as-a-Service (MaaS) in the UAE: A Market Segmentation Study
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Sulafa Badi, Salam Khoury, Kholoud Yasin, Khalid Al Marri
Abstract - This study investigates consumer attitudes toward Mobility as a Service (MaaS) in the context of the UAE's diverse population, focusing on the factors influencing adoption intentions. A survey of 744 participants was conducted to assess public perceptions, employing hierarchical and non-hierarchical clustering methods to identify distinct consumer segments. The analysis reveals five clusters characterised by varying demographics, travel lifestyles, and attitudes towards MaaS, highlighting the influence of UTAUT2 variables, including performance expectancy, social influence, hedonic motivation, price value, and perceived risk. Among the clusters, ‘Enthusiastic Adopters’ and ‘Convenience-Driven Adopters’ emerge as key segments with a strong reliance on public transport and a willingness to adopt innovative transportation solutions. The findings indicate a shared recognition of the potential benefits of MaaS despite differing opinions on its implementation. This research contributes to the theoretical understanding of MaaS adoption by offering an analytical typology relevant to a developing economy while also providing practical insights for policymakers and transport providers. By tailoring services to meet the unique needs of various consumer segments, stakeholders can enhance the integration of MaaS technologies into the UAE's transportation system. Future research should explore the dynamic nature of public sentiment regarding MaaS to inform ongoing development and implementation efforts.
Paper Presenters
avatar for Salam Khoury

Salam Khoury

United Arab Emirates
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Deep Learning Based Approach for Identifying Forged Handwritten Signatures- A Literature Survey
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Rakhi Bharadwaj, Priyanshi Patle, Bhagyesh Pawar, Nikita Pawar, Kunal Pehere
Abstract - The detection of forged signatures is a critical challenge in various fields, including banking, legal documentation, and identity verification. Traditional methods for signature verification rely on handcrafted features and machine learning models, which often struggle to generalize across varying handwriting styles and sophisticated forgeries. In recent years, deep learning techniques have emerged as powerful tools for tackling this problem, leveraging large datasets and automated feature extraction to enhance accuracy. In this literature survey paper, we have studied and analyzed various research papers on fake signature detection, focusing on the accuracy of different deep learning techniques. The primary models reviewed include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). We evaluated the performance of these methods based on their reported accuracy on benchmark datasets, highlighting the strengths and limitations of each approach. Additionally, we discussed challenges such as dataset scarcity and the difficulty of generalizing models to detect different types of forgeries. Our analysis provides insights into the effectiveness of these methods and suggests potential directions for future research in improving signature verification systems.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Deep Learning Based Channel Coding for Point-to-Point and Point-to-Multipoint Communication in Advanced Cellular Network
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Shilpa Bhairanatti, Rubini P
Abstract - While the rollout of 5G cellular networks will extend into the next decade, there is already significant interest in the technologies that will form the foundation of its successor, 6G. Although 5G is expected to revolutionize our lives and communication methods, it falls short of fully supporting the Internet-of-Everything (IoE). The IoE envisions a scenario where over a million devices per cubic kilometer, both on the ground and in the air, demand ubiquitous, reliable, and low-latency connectivity. 6G and future technologies aim to create a ubiquitous wireless connectivity for entire communication system. This development will accommodate the rapidly increasing number of intelligent devices and communication demand. These objectives can be achieved by incorporating THz band communication, wider spectrum resources with minimized communication error. However, this communication technology faces several challenges such as energy efficiency, resource allocation, latency etc., which needs to be addressed to improve the overall communication performance. To overcome these issues, we present a roadmap for Point to Point (P2P) and Point-to-Multipoint (P2MP) communication where channel coding mechanism is introduced by considering Turbo channel coding scheme as base approach. Furthermore, deep learning based training is provided to improve the error correcting performance of the system. The performance of proposed model is measured in terms of BER for varied SNR levels and additive white noise channel distribution scenarios, where experimental analysis shows that the proposed coding approach outperformed existing error correcting schemes.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Efficient and Secure Data Storage Solutions for UAVs
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Hiep. L. Thi
Abstract - A brief summary of the paper, highlighting key points such as the increasing role of UAVs in various sectors, the challenges related to data storage on UAVs, and proposed solutions for improving both the efficiency and security of data management. Include a note on the scope of the study, methodologies, and key findings.
Paper Presenters
avatar for Hiep. L. Thi
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Key Considerations For Appropriate Information In A Communication Architecture For Smart Manufacturing
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Gareth Gericke, Rangith B. Kuriakose, Herman J. Vermaak
Abstract - Communication architectures are demonstrating their significance in the development landscape of the Fourth industrial revolution. Nonetheless, the progress of architectural development lags behind that of the Fourth industrial revolution itself, resulting in subpar implementations and research gaps. This paper examines the prerequisites of Smart Manufacturing and proposes the utilization of a novel communication architecture to delineate a pivotal element, information appropriateness, showcasing its efficient application in this domain. Information appropriateness, leverages pertinent information within the communication flow at a machine level facilitating real-time monitoring, decision-making, and control over production metrics. The metrics scrutinized herein include production efficiency, bottleneck mitigation, and network intelligence, while accommodating architectural scalability. These metrics are communicated and computed at a machine level to assess the efficacy of a communication architecture at this level, while also investigating its synergistic relationship across other manufacturing tiers. Results of this ongoing study shed insights into data computation and management at the machine level and demonstrate an effective approach for handling pertinent information at critical junctures. Furthermore, the adoption of a communication architecture helps minimize information redundancy and overhead in both transmission and storage for machine level communication.
Paper Presenters
avatar for Gareth Gericke

Gareth Gericke

South Africa
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Tales From the Algorithm: Enhancing Reading Comprehension Assessments with AI-Generated Arabic Stories
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Y. Abdelghafur, Y. Kaddoura, S. Shapsough, I. Zualkernan, E. Kochmar
Abstract - Early reading comprehension is crucial for academic success, involving skills like making inferences and critical analysis, and the Early Grade Reading Assessment (EGRA) toolkit is a global standard for assessing these abilities. However, creating stories that meet EGRA's standards is time-consuming and labour-intensive and requires expertise to ensure readability, narrative coherence, and educational value. In addition, creating these stories in Arabic is challenging due to the limited availability of high-quality resources and the language's complex morphology, syntax, and diglossia. This research examines the use of large language models (LLMs), such as GPT-4 and Jais, to automate Arabic story generation, ensuring readability, narrative coherence, and cultural relevance. Evaluations using Arabic readability formulas (OSMAN and SAMER) show that LLMs, particularly Jais and GPT, can effectively produce high-quality, age-appropriate stories, offering a scalable solution to support educators and enhance the availability of Arabic reading materials for comprehension assessment.
Paper Presenters
avatar for Y. Kaddoura

Y. Kaddoura

United Arab Emirates
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room A London, United Kingdom

9:30am GMT

Artificial Intelligence Tools for Condition Monitoring of Mechanical Processes
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Robert Johnson, Jing Jung Zhang, Fu Kuo Manchu, Silvio Simani
Abstract - With a focus on fixing the common problems of imbalance and misalignment, this study introduces an artificial intelligence tool based on a state-of-the-art deep learning method that will enhance automatic condition monitoring and fault detection for mechanical processes. The main breakthrough is a trustworthy model for condition monitoring using artificial neural networks that extract feature vectors from signal data using frequency analysis. A high fault detection accuracy rate highlights the research accomplishment, proving its ability to establish new solutions also for predictive maintenance. This research considers the different working conditions of a mechanical process by analysing four separate operational classes, including balanced operation, horizontal vertical misalignments, unbalanced situations, and regular operation. The dataset studied in this work includes a wealth of information and was carefully calibrated for neural network training, which has also the potential to be employed in the development of maintenance procedures for mechanical plants. Finally, this study provides a significant step towards the goals of improved performance and unyielding safety requirements that industries are aiming for.
Paper Presenters
avatar for Robert Johnson

Robert Johnson

United Kingdom
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Characteristics of handicraft businesses and the integration of Lean Manufacturing within these units in the Medina of Fes
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Nejjari Nada, Chafi Anas, Kammouri Alami Salaheddine
Abstract - The handicraft sector holds crucial importance within the Moroccan economy, serving as a fundamental pillar that significantly contributes to the country's economic balance. This sector not only preserves cultural heritage but also provides employment opportunities and sustains local economies. Our study primarily revolves around an in-depth exploration of the artisanal universe, aiming to derive relevant recommendations to optimize its performance and enhance its competitive edge. By focusing on identifying gaps, challenges, and opportunities within the sector, our goal is to develop concrete improvement suggestions that can catalyze continuous development and growth. Through a comprehensive analysis, we seek to provide actionable insights that can improve efficiency, sustainability, and the overall impact of the handicraft sector on the Moroccan economy. This research aspires to support policymakers, stakeholders, and artisans themselves in fostering a thriving and resilient artisanal industry that contributes robustly to economic and social development.
Paper Presenters
avatar for Nejjari Nada
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Ubayd Bapoo, Clement N Nyirenda
Abstract - This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is on parametrized action (PA) spaces, eliminating the need for recurrent networks, with benchmarks Platformv0 and Goal-v0 testing discrete actions linked to continuous actionparameter spaces. Hyperparameter optimization was performed with Microsoft NNI, ensuring reproducibility by modifying the codebase for GAC and TQC. Results show that Parameterized Action Greedy Actor-Critic (PAGAC) outperformed other algorithms, achieving the fastest training times and highest returns across benchmarks, completing 5,000 episodes in 41:24 for the Platform game and 24:04 for the Robot Soccer Goal game. Its speed and stability provide clear advantages in complex action spaces. Compared to PASAC and PATQC, PAGAC demonstrated superior efficiency and reliability, making it ideal for tasks requiring rapid convergence and robust performance. Future work could explore hybrid strategies combining entropy-regularization with truncation-based methods to enhance stability and expand investigations into generalizability.
Paper Presenters
avatar for Ubayd Bapoo

Ubayd Bapoo

South Africa
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Interactive Websites for Sustainable Pharmaceutical Governance and Collaboration
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Amalia Mukhlas, Shahrinaz Ismail, Bazilah A. Talip, Jawahir Che Mustapha, Juliana Jaafar
Abstract - The pharmaceutical industry’s significant influence on other sectors underscores the urgency of implementing sustainable systems. Technology offers invaluable tools for achieving this goal. This study examines the challenges faced by pharmaceutical websites to function in collaboration and governance, emphasizing the difficulties in providing accessible and relevant information. Using experiential observation, it highlights governance-related inefficiencies in website design. The proposed solutions focus on improving the website usability for transparency, accountability and social involvement in line with sustainable systems practices. The findings disclose the suboptimal design of many pharmaceutical websites, hindering collaboration with external parties, specifically academic and potentially impact the industry’s sustainable efforts.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

IoT Based Framework for Cholera Disease Early Detection in Yemen
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Sarah Abdalrahman Al-Shqaqi, Mohammed Zayed, Kamal Al-Sabahi, Adnan Al-Mutawkkil
Abstract - The development of the Internet of Things (IoT) in recent years has significantly contributed to a paradigm change in all aspects of life. IoT has rapidly gained traction in a short period of time across a variety of sectors, including business, healthcare, governance, infrastructure management, consumer services, and even defense. IoT has the ability to monitor systems through the delivery of consistent and precise information. In medical services it enables us to make decisions, surrounding technologies will play an essential contribution to providing healthcare to people in remote locations. The health centers gather data from areas where cholera has emerged or is suspected, which is then sent to the ministry of public health and population. The world health organization in Yemen analyzes the data using two systems (Edews and Ewars), but they lack the full capability for early detection of cholera, and do not utilize Internet of Things technology, which plays an important role in solving most health problems, including cholera. To address this issue, we proposed a framework consists of 6 layers and add parameters that helps early detection of cholera disease. In this study, IoT framework was used for early detection of cholera, thereby assisting the ministry of public health and population and the world health organization in making informed decisions by add the intelligent medical server layer. Overall, this framework is applicable to any field, particularly healthcare for cholera.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Securing UAV Communications using Lightweight Cryptography Algorithms
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Praveen Kumar Sandanamudi, Neha Agrawal, Nikhil Tripathi, Pavan Kumar B N
Abstract - Over the past few years, the proliferation of resource-constrained devices and the looming of Internet-of-Things (IoT) for UAV applications has accentuated the need for lightweight cryptographic (LWC) algorithms. These algorithms are designed to be more suitable for UAV application based IoT devices as they are efficient in terms of memory usage, computation, utilization of power, etc. Based on the literature study, the algorithms mostly suitable for the UAV application based resource-constrained devices are identified in this paper. This list also includes ASCON cipher, winner of the NIST’s lightweight cryptography standardization contest. Furthermore, these algorithms have been implemented on a UAV application based Raspberry Pi 3 Model B to analyze their hardware and software performance w.r.t the essential metrics, such as latency, power consumption, throughput, energy consumption, etc., for different payloads. From the experimental results, it has been observed that the SPECK is optimized for software implementations and may offer better performance in certain scenarios, especially on UAV application based resource-constrained devices. ASCON, on the other hand, provides both encryption and authentication in a single pass, potentially reducing latency and overhead. This paper aims to assist researchers in pinpointing the most appropriate LWC algorithm tailored to specific scenarios and requirements.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room B London, United Kingdom

9:30am GMT

Analysing U.S. Congressional History with Python: Insights from the 66th to 118th Congresses and Generational Trends
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Venkata Sai Varsha, Prodduturi Bhargavi, Samah Senbel
Abstract - This paper provides a thorough analysis of U.S. Congressional history from the 66th to the 118th Congress, examining demographic trends, political shifts, and party dynamics across decades. Using Python-based data processing, the study compiles and interprets historical data to identify patterns in representative demographics, party representation, and legislative impact. The analysis investigates generational changes within Congress, with particular focus on age distribution, tenure, and shifts in political party dominance. Visualizations and statistical insights generated through Python libraries, such as Pandas, Matplotlib, and Seaborn, reveal significant historical events and socio-political influences shaping Congress. By examining age-related trends, the study highlights a generational gap, with older members retaining significant representation and a younger cohort gradually emerging. Additionally, it explores the evolution of bipartisan dominance and third-party representation, offering insights into political diversity and the resilience of the two-party system. This research contributes to the understanding of how demographic and political transformations within Congress reflect broader societal trends and may influence future governance.
Paper Presenters
avatar for Samah Senbel

Samah Senbel

United States of America
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Comparative Analysis on Predicting Price Hike with Sources Using Different Machine Learning Algorithms
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Taufique Hedayet, Anup Sen, Mahfuza Akter Jarin, Shohel Rana Shaon, Joybordhan Sarkar, Sadah Anjum Shanto
Abstract - A price hike is an atypical increase in the cost of an essential item. A price rise is an unusual increase in the prices of everyday basic goods. The price increase has several factors. Everyday items are becoming more and more expensive. In this research, we have used Bidirectional Long Short-Term Memory (BLSTM), Long Short-Term Memory (LSTM), Adaboost, Support Vector Regression (SVR), Gradient Boosted Regression Tree (GBRT), and REST API for forecasting the prices for necessary commodities and we will evaluate efficiency by the value of gold. Our preeminent objective is to find a method that can detect and predict price hike that can be much more accurate and efficient than the other approaches that are currently available in the relevant literature. The acceptance of the detection and prediction is based on their accuracy and efficiency. Price hike predictions may role important for everyday life for many stakeholders, including firms, consumers, and government. The energetic and sporadic character of advertising estimating is highlighted as a major foreseeing.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Northeastern United States Traffic Accident Trends: a Geospatial and Statistical Analysis using Python
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Sathvik Putta, Tejagni Chichili, Samah Senbel
Abstract - Traffic accidents remain a critical issue globally, with significant implications for public health, safety, and economic stability. This study provides a comprehensive analysis of traffic accident trends in the northeastern United States, focusing on Connecticut and its neighboring states—New York, New Jersey, New Hampshire, and Massachusetts. By leveraging a dataset encompassing fatal collisions, driver behaviors, and car insurance premiums, this work investigates correlations between risky driving habits, accident outcomes, and the associated financial impacts. Key metrics analyzed include speeding-related incidents, alcohol-impaired driving, distracted driving, and their influence on insurance costs and claims. rigorous data preprocessing methodology was employed, including normalization, outlier detection, and feature selection, ensuring a robust and reliable dataset for analysis. The study used advanced visualization techniques and statistical modeling, utilizing Python libraries like Pandas, Matplotlib, and Scikit-learn, to identify trends and derive actionable insights. Comparative analysis reveals that while neighboring states such as Massachusetts and New York excel in certain safety metrics, Connecticut lags in addressing critical behavioral risks like speeding and alcohol impairment.
Paper Presenters
avatar for Samah Senbel

Samah Senbel

United States of America
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

PREDICTING BREAST CANCER RECURRENCE USING HYBRID MACHINE LEARNING ALGORITHMS: A STUDY ON MIZORAM STATE CANCER INSTITUTE DATA
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Dawngliani M S, Thangkhanhau H, Lalhruaitluanga
Abstract - Breast cancer continues to pose a major public health challenge world-wide, necessitating the development of accurate prediction algorithms to improve patient outcomes. This study aimed to devise a predictive model for breast cancer recurrence using machine learning techniques, with data sourced from the Mizoram State Cancer Institute. Utilizing the Weka machine learning toolkit, a hybrid approach incorporating classifiers such as K-Nearest Neighbors (KNN) and Random Forest was explored. Additionally, individual classifiers including J48, Naïve Bayes, Multilayer Perceptron, and SMO were employed to evaluate their predictive efficacy. Voting ensembles are utilized to augment performance accuracy. The hybridization of Random Forest and KNN classifiers, along with other base classifiers, demonstrated notable improvements in predictive performance across most classifiers. In particular, the combination of Random Forest with J48 yielded the highest performance accuracy at 82.807%. However, the J48 classifier alone achieved a superior accuracy rate of 84.2105%, signifying its efficacy in this context. Thus, drawing upon the analysis of the breast cancer dataset from the Mizoram State Cancer Institute, Aizawl, it was concluded that J48 exhibits the highest predictive accuracy compared to alternative classifiers.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Preliminary Assessment of a UTAUT-Based User Acceptance Model for KeyDESK: A Facility Management System in Healthcare
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Cansu Cigdem EKIN, Mehmet Afsin YUCE, Emrah EKMEN, Gokay GOK, Ibrahim UGUR
Abstract - This study presents a preliminary assessment of the reliability and validity of a technology acceptance model UTAUT (Unified Theory of Acceptance and Use of Technology) for KeyDESK, a health facility management system used in healthcare settings. The model evaluates key constructs of the UTAUT model to better understand the contextual adoption of health facility management systems. Data were collected from 2547 respondents comprising system operators and healthcare professionals who utilize the KeyDESK platform for task and service management. Reliability was assessed through internal consistency measures, which confirmed strong alignment across constructs. Convergent validity was established by evaluating shared variance and item relevance, while the distinctiveness of constructs was verified through cross-comparative analyses. Preliminary results suggest that all constructs fulfill reliability and validity criteria, ensuring the robustness of the measurement model. These results provide an empirical foundation for understanding user acceptance of health facility management systems and highlight areas for further model refinement. This study serves as a critical step towards conducting more comprehensive structural equation modeling (SEM) analyses in subsequent research.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Relative Learning Contents Difficulty Analytics System between Learner and Learning Contents
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Kwang Sik Chung, Jihun Kang
Abstract - As distance education services develop, much research is being conducted to analyze learners' learning activities and provide a customized learning environment optimized for each individual learner. The personalized learning environment is basically determined based on learner-centered learning analytics. However, learning analysis research on learning content, which is the subject of interaction with learners, is insufficient. In order to recommend learning content to learners and provide the most appropriate learning evaluation method, learner's learning capability and the difficulty of the learning content must be appropriately analyzed. In this research, the relative learning difficulty of the learning contents and the learner is analyzed, and through this, the learner-relative learning contents difficulty is analyzed. For this purpose, educational (learning) contents Data, Learning Operational Data, Learner Personal Learning data, Peer Learner Group Data, and Learner Statistical Data are collected, stored at learning records storage server and analyzed by the Learning Analytics System with several Deep Learning models. Finally, we find the absolute difficulty of the subject, the relative difficulty of the subject, the relative difficulty of the peer learner group, the relative learning capability of a learner, the absolute learning capability of a learner, the learning contents relative difficulty level for each learner, and the absolute difficulty of the subject for each individual learner, and personalized learning contents are created and decide with them.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room C London, United Kingdom

9:30am GMT

Clustering by an Evolutionary Random Swap Algorithm
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Libero Nigro, Franco Cicirelli
Abstract - This paper proposes the Evolutionary Random Swap (ERS) clustering algorithm that extends the basic behavior of Random Swap (RS) by a population of candidate solutions (centroid configurations), preliminarily established through a proper seeding procedure, which provides the swap data points that RS uses in the attempting step of improving the current clustering solution. The new centroid solution improves the previous solution in the case it reduces the Sum of Squared Errors (SSE) index. ERS, though, can also be used to optimize (maximize), in not large datasets, the Silhouette (SI) coefficient which measures the degree of separation of clusters. High-quality clustering is mirrored by clusters with high internal cohesion and a high external separation. The paper describes the design of ERS that is currently implemented in parallel Java. Different clustering experiments concerning the application of ERS to both benchmark and real-world datasets are reported. Clustering results can be compared, for accuracy and execution time performance, to the use of the basic RS algorithm. Clustering quality is also checked with the application of other known algorithms.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Example Guided Prompt Tuning for Sentiment Analysis of Code-Switched Hindi and Dravidian Languages on Llama
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Amir Ince, Saurav Keshari Aryal, Howard Prioleau
Abstract - With the rise of social media, vast amounts of text, including code-switching, are being generated, presenting unique linguistic challenges for sentiment analysis. This study explores how existing models perform without fine-tuning to understand the challenges of analyzing code-switched data. We propose a prompt tuning approach based on generated versus human-labeled code-switched dataset. Our results show that the Few-shot technique and the Prompt Optimization Framework with Dataset Examples offer the most consistent performance, highlighting the importance of real-world examples and language-specific data in improving multilingual sentiment analysis. However, the studied models and technique do no exhibit the ability to significant triage sentiments for Hindi and Dravidian languages.
Paper Presenters
avatar for Amir Ince

Amir Ince

United States of America
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Exploring Prompt Engineering for Generating G code to program CNC Milling Machines
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Hector Rafael Morano Okuno
Abstract - The use of large language models (LLMs) has spread to various areas of knowledge. However, it is necessary to continue exploring them to determine their scope. In this work, an LLM is investigated to generate G-code programs for machine parts in Computer numerical control (CNC) milling machines. Prompt Engineering is employed to communicate with LLM, and a series of prompts are used to inquire about its scope. Among the results are the manufacturing operations that an LLM can program and the problems that arise in the developed G-codes. Finally, a sequence of steps is proposed to create G-codes using LLMs, and the prompt structures are shown to help users understand how the LLMs work when generating G-codes.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Fuzzy Deep Learning Feature-Based Classification using Transmission Casing Data
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Hanaa Mohsin Ahmed, Muna Ghazi Abdulsahib
Abstract - Fuzzy deep learning, which combines fuzzy logic and deep learning techniques to handle uncertainty and imprecision in the data as a first task and learn hierarchical representations of the data as a second task, is a promising method for feature data classification method with many usefully and important applications that meagres with several disciplines of knowledge. This work uses a fuzzy logic deep learning model to classify feature data on transmission casing data in specific. For the first time as an approach, fuzzy logic deep learning has been used to use transmission casing data, a well-known benchmark dataset application for classification tasks in specific. The results of the experiments show that the proposed model outperforms the deep learning-based classification model, classifying the transmission casing data with a higher accuracy of 100% and more robustness. We also go over potential future research directions for Transmission-based fuzzy deep learning feature data classification.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Integration of Artificial Intelligence into Battery Energy Storage System Fault Diagnosis: A Review
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Titi Andriani, Chairul Hudaya, Iwa Garniwa
Abstract - The transition toward more sustainable renewable energy sources has driven advancements in energy storage technology, including the development of Battery Energy Storage Systems (BESS). To improve the reliability and efficiency of BESS, implementing an effective monitoring system is essential, especially for detecting and diagnosing battery faults. The most commonly utilized methodologies for the diagnosis of faults in battery systems encompass knowledge-based, model-based, and data-based approaches. Artificial Intelligence (AI) holds significant potential to enhance fault diagnosis systems through predictive models capable of analyzing large datasets, identifying patterns, and forecasting potential faults. This work offers a thorough investigation of AI applications for BESS fault diagnosis, supported by an in-depth review of reliable sources such as Science Direct, IEEE Xplore, and Scopus. A total of 723 papers from scientific publications over the last five years were initially considered in this research. Following a rigorous screening process, including duplicate removal and the application of exclusion and inclusion criteria, 28 studies were selected for quantitative analysis. This study not only examines the types of faults that can be diagnosed but also assesses the challenges associated with recent advancements in this technology. In this context, the research identifies several aspects that have been applied within the theory of AI-based fault diagnosis for BESS and offers recommendations for further research. The results of this study are intended to aid in the creation of fault diagnosis systems that are more dependable and effective, which in turn will support the transition to cleaner and more sustainable energy.
Paper Presenters
avatar for Titi Andriani

Titi Andriani

Indonesia
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

On the Modelling of Living Matter. What Code Does Nature Operate In?
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Vasyl Yurchyshyn, Yaroslav Yurchyshyn
Abstract - A living organism can be seen as a tool designed to perform specific functions, while both living and non-living matter represent distinct manifestations of nature. This work proposes considering living and non-living matter as physical systems, integrating existing scientific and technological advancements in the fields of physics, biology, and computer science. It suggests that scientific and technological developments in physical systems can also be applied to biological systems. The work addresses issues related to coding within living organisms and physical systems, and explores potential models for their functioning. The use of the golden ratio in living organisms and the potential benefits of applying these codes to physical systems are examined. Additionally, the refinement of physical quantities using the approaches discussed is addressed. Key issues in the modelling of living matter are highlighted, and various approaches to addressing these challenges are explored. The binary encoding and encoding based on π, e, and the golden ratio are considered.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room D London, United Kingdom

9:30am GMT

Attentional Convolutional Architecture for Brain Tumor Recognition
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Shahd Tarek, Ali Hamdi
Abstract - Brain tumors represent one of the most critical health challenges due to their complexity and high mortality rates, necessitating early and precise diagnosis to improve patient outcomes. Traditional MRI interpretation methods rely heavily on manual analysis, which is timeconsuming, error-prone, and inconsistent. To address these limitations, this study introduces a novel deep attentional framework that integrates multiple Convolutional Neural Network (CNN) base models—EfficientNet- B0, ResNet50, and VGG16—within a Multi-Head Attention (MHA) mechanism for robust brain tumor classification. Convolutional features extracted from these CNNs are fed into the MHA as Query (Q), Key (K), and Value (V) inputs, enabling the model to focus on the most distinguishing features within MRI images. By leveraging complementary feature maps from diverse CNN architectures, the MHA mechanism generates more refined, attentive representations, significantly improving classification accuracy. The proposed approach classifies MRI images into four categories: pituitary tumor, meningioma, glioma, and no tumor. A dataset of 7,023 labeled MRI images was curated from public repositories, including Figshare, SARTAJ, and Br35H, with preprocessing steps to standardize dimensions and remove margins. Experimental results demonstrate the superior performance of individual CNNs—VGG16 achieving 97.25% accuracy, ResNet50 98.02%, and EfficientNet-B0 93.21%. Moreover, the ensemble model integrating VGG16, EfficientNet-B0, and ResNet50 achieves the highest accuracy of 98.70%, surpassing other ensemble configurations such as ResNet50 + VGG16 + EfficientNet-B0 (96.95%) and VGG16 + ResNet50 + EfficientNet-B0 (95.96%). These findings underscore the effectiveness of multi-level attention in refining predictions and provide a reliable, automated tool to assist radiologists. The proposed framework highlights the transformative potential of deep learning in medical imaging, streamlining clinical workflows, and enhancing healthcare outcomes.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Enhancing English Learning in Primary Education: Evaluating SCRATCH as a Pedagogical Tool
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Luis Puebla Rives, Connie Cofre-Morales, Miguel Rodriguez, Eduardo Puraivan, Marisela Vera Medinelli, Abigail Gonzalez, Ignacio Reyes, Karina Huencho-Iturra, Macarena Astudillo-Vasquez
Abstract - This study analyzes the perception of both practicing and future English teachers regarding an activity designed under a didactic conceptual framework that uses SCRATCH as a tool to promote English language teaching to 4th grade primary school students. A survey was designed, validated by experts, and then applied to 28 participants. The reliability of the scale was analyzed, showing internal consistency of 0.96 and 0.99 using Cronbach’s alpha and G6, respectively. Implicative statistical analysis was used to explore the relationships between questions across different dimensions. The similarity tree identified two significant clusters with values of 0.6 and 0.54. The implicative graph and cohesive tree displayed implications with values exceeding 0.7. The findings highlight a high appreciation for the activity using SCRATCH, which is perceived as both viable and an effective facilitator of contextualized and meaningful learning.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Enhancing Warehouse Efficiency with a Decentralized UWB and IoT Tracking System
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - La-or Kovavisaruch, Kriangkri Maneerat, Taweesak Sanpechuda, Krisada Chinda, Sodsai Wisadsud, Thitipong Wongsatho, Sambat Lim, Kamol Kaemarungsi, Tiwat Pongthavornkamol
Abstract - The industrial sector in Thailand remains primarily characterized by traditional practices of Industry 2.0, which face significant challenges in transitioning to Industry 4.0. This research proposes a decentralized real-time location and status reporting system to address these issues. By utilizing Ultra-Wideband (UWB) technology combined with the Internet of Things (IoT), the newly developed "UWB Tag Plus" device eliminates the reliance on costly UWB gateways, instead transmitting data directly to cloud servers via 4G/5G networks. Implementing this system at an automotive parts assembly factory in Thailand reduced system costs by over 30%. The communications protocol between the tag and cloud server changed from IEEE 802.15.4 to TCP/IP, which enhanced operational flexibility. The proposed system makes advanced modernization more accessible for small and medium-sized enterprises. Furthermore, the "UNAI Data Analytic" tool provides real-time performance analytics for automated guided vehicles, empowering warehouse operators to optimize operations and improve efficiency.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Jailbreak Attack on a Multi-Agent LLM Defense System
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Junichiro Ando, Satoshi Okada, Takuho Mitsunaga
Abstract - Large Language Models (LLMs) like ChatGPT and Claude have demonstrated exceptional capabilities in content generation but remain vulnerable to adversarial jailbreak attacks that bypass safety mechanisms to output harmful content. This study introduces a novel jailbreak method targeting Autodefense, a multi-agent defense framework designed to detect and mitigate such attacks. By combining obfuscation techniques with the injection of harmless plaintext, our proposed method achieved a high jailbreak attack success rate (maximum value is 95.3%) across different obfuscation methods, which marks a significant increase compared to the ASR of 7.95% without our proposed method. Our experiments prove the effectiveness of our proposed method to bypass Autodefense system.
Paper Presenters
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

Sentiment Analysis of Tourism Village Amalgam from Online Review Platform Using Large Language Models
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Hana Ulinnuha, Mukhlish Rasyidi, Yanti Tjong, Husna Putri Pertiwi, Wendy Purnama Tarigan, Michael Tegar Wicaksono
Abstract - Recently, tourism villages are central to Indonesia’s tourism development strategy, contributing significantly to local, regional, and even national economic growth. With the increasing number of tourism villages, understanding tourists’ perspectives is essential for ensuring their sustainability. Tourist reviews on platforms provide valuable insights into their experiences and expectations. Sentiment analysis, widely used in tourism research, enables the extraction and identification of opinions from these unstructured data sources, offering a deeper understanding of visitor sentiments. This study employs Large Language Models (LLM) to analyze tourist reviews of Indonesian tourism villages. Unlike common methods, LLMs provide advanced capabilities for both sentiment analysis and the evaluation of the 4A tourism components—Attraction, Accessibility, Amenities, and Ancillary services. By examining positive, neutral, and negative reviews, the research identifies key factors that shape tourist experiences. The findings offer practical recommendations for tourism village managers, not only to enhances visitor satisfaction but also supports the government’s goal of fostering economic growth in tourism and rural areas. The study demonstrates the potential of LLM-based sentiment analysis as a valuable tool for advancing Indonesia's tourism industry.
Paper Presenters
avatar for Hana Ulinnuha

Hana Ulinnuha

Indonesia
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

9:30am GMT

The Impact of Social Media Usage on the Mental Health and Social Behaviour of Young Adults: A Systematic Literature Review
Friday February 21, 2025 9:30am - 11:00am GMT
Authors - Mark Bhunu, Timothy T Adeliyi
Abstract - The proliferation of social media (SM) platforms has made them an integral part of our daily lives, significantly shaping how we interact and engage with the world. While SM offers benefits such as social connectedness and sup-port, its impact on the psychological health and well-being of young individuals has both positive and negative dimensions. Understanding these effects is essen-tial to developing strategies for mitigating the adverse outcomes associated with its use. A systematic literature review was conducted to explore the influence of SM usage on the mental health and well-being of young adults aged 18 to 35. Drawing insights from 25 publications across three databases, the study identified common themes related to SM's effects on this demographic. The findings reveal a correlation between SM use and mental health outcomes, with benefits includ-ing enhanced social support but also risks such as depression, anxiety, low self-esteem, and increased vulnerability to cyberbullying. These results highlight the urgent need for targeted interventions to address the negative consequences of SM on the mental health and overall well-being of young adults.
Paper Presenters
avatar for Timothy T Adeliyi

Timothy T Adeliyi

South Africa
Friday February 21, 2025 9:30am - 11:00am GMT
Virtual Room E London, United Kingdom

11:00am GMT

Session Chair Remarks
Friday February 21, 2025 11:00am - 11:03am GMT
Friday February 21, 2025 11:00am - 11:03am GMT
Virtual Room A London, United Kingdom

11:00am GMT

Session Chair Remarks
Friday February 21, 2025 11:00am - 11:03am GMT
Friday February 21, 2025 11:00am - 11:03am GMT
Virtual Room B London, United Kingdom

11:00am GMT

Session Chair Remarks
Friday February 21, 2025 11:00am - 11:03am GMT
Friday February 21, 2025 11:00am - 11:03am GMT
Virtual Room C London, United Kingdom

11:00am GMT

Session Chair Remarks
Friday February 21, 2025 11:00am - 11:03am GMT
Friday February 21, 2025 11:00am - 11:03am GMT
Virtual Room D London, United Kingdom

11:00am GMT

Session Chair Remarks
Friday February 21, 2025 11:00am - 11:03am GMT
Friday February 21, 2025 11:00am - 11:03am GMT
Virtual Room E London, United Kingdom

11:03am GMT

Closing Remarks
Friday February 21, 2025 11:03am - 11:05am GMT
Friday February 21, 2025 11:03am - 11:05am GMT
Virtual Room A London, United Kingdom

11:03am GMT

Closing Remarks
Friday February 21, 2025 11:03am - 11:05am GMT
Friday February 21, 2025 11:03am - 11:05am GMT
Virtual Room B London, United Kingdom

11:03am GMT

Closing Remarks
Friday February 21, 2025 11:03am - 11:05am GMT
Friday February 21, 2025 11:03am - 11:05am GMT
Virtual Room C London, United Kingdom

11:03am GMT

Closing Remarks
Friday February 21, 2025 11:03am - 11:05am GMT
Friday February 21, 2025 11:03am - 11:05am GMT
Virtual Room D London, United Kingdom

11:03am GMT

Closing Remarks
Friday February 21, 2025 11:03am - 11:05am GMT
Friday February 21, 2025 11:03am - 11:05am GMT
Virtual Room E London, United Kingdom

11:43am GMT

Opening Remarks
Friday February 21, 2025 11:43am - 11:45am GMT
Friday February 21, 2025 11:43am - 11:45am GMT
Virtual Room A London, United Kingdom

11:43am GMT

Opening Remarks
Friday February 21, 2025 11:43am - 11:45am GMT
Friday February 21, 2025 11:43am - 11:45am GMT
Virtual Room B London, United Kingdom

11:43am GMT

Opening Remarks
Friday February 21, 2025 11:43am - 11:45am GMT
Friday February 21, 2025 11:43am - 11:45am GMT
Virtual Room C London, United Kingdom

11:43am GMT

Opening Remarks
Friday February 21, 2025 11:43am - 11:45am GMT
Friday February 21, 2025 11:43am - 11:45am GMT
Virtual Room D London, United Kingdom

11:45am GMT

AI-powered exploration of butterfly plant interaction for industrial farming
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Md Fahim Afridi Ani, Abdullah Al Hasib, Munima Haque
Abstract - This research explores the possibility of improving insect farming by integrating Artificial Intelligence (AI) unlocking the complicated relationship between butterflies and plants they pollinate to reconsider the way species are classified and helping to redraw farming practices for the butterflies. Traditional methods of butterfly classification are morphologically and behaviorally intensive, thus mostly very time-consuming to conduct considering that most of them have a high level of subjective interpretation. We therefore apply our approach to ecological interactions involving butterfly species and their respective plants for efficient data-driven solutions. This also focuses on the application of AI in making full benefits from butterfly farming, trying to determine where each species will be best located. The system will, therefore, classify and manage butterflies with much more ease, saving time and energy usually used in conventional classification methods hence on to the farmer or industrial client. The research deepens the understanding of insect-plant relationships for better forecasting of butterfly behavior and, therefore, healthier ecosystems through optimized pollination and habitat balance. For that purpose, a dataset of butterfly species and related plants was developed, on which machine learning models were applied, including decision trees, random forests, and neural networks. It tuned out that the neural network outperformed the others with an accuracy of 93%. Apart from classification, it helps in the identification of a habitat to provide the best conditions possible for the rearing of butterflies. Application of AI in this field simplifies the work of butterfly farming hence being an important tool to be used in improving growth and conservation of biodiversity. Integrating machine learning into ecological research and industry provides scalable, time-efficient solutions for the classification of species toward the sustainable farming of butterflies.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Collaborative product innovation model with large language models and agentic systems
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan
Abstract - Product innovation is critical in strategizing business decisions in highly-competitive markets. For product enhancements, the entrepreneur must garner data from a target demographic through research. A solution to this involves qualitative customer feedback. The study proposes the viability of artificial intelligence (AI) as a co-pilot model to simulate synthetic customer feedback with agentic systems. Prompting with ChatGPT-4o’s homo silicus attribute can generate feedback on certain business contexts. Results show that large language models (LLM) can generate qualitative insights to utilize in product innovation. Results seem to generate human-like responses through few-shot techniques and Chain-of-Thought (CoT) prompting. Data was validated with a Python script. Cosine similarity tested the similarity of datasets to quantify the juxtaposition of synthetic and actual customer feedback. This model can be essential in reducing the total resources needed for product evaluation through preliminary analysis, which can help in sustainable competitive advantage.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Enhancing Sarcastic Language Detection by Exploring the Potential of Machine Learning and Deep Learning Approaches
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Jeehaan Algaraady, Mohammad Mahyoob Albuhairy
Abstract - Sarcasm, a sentiment often used to express disdain, is the focus of our comprehensive research. We aim to explore the effectiveness of various machine learning and deep learning models, such as Support Vector Machine (SVM), Recurrent Neural Networks (RNN), Bidirectional Long Short-Term Memory (BiLSTM), and fine-tuning pre-trained transformer-based mode (BERT) models, for detecting sarcasm using the News Headlines dataset. Our thorough framework investigates the impact of the DistilBert method for text embeddings on enhancing the accuracy of the DL models (RNN and LSTM) for training and classification. To assess the highest values of the proposed models, the authors utilized the four-performance metrics: F1 score, recall, precision, and accuracy. The outcomes revealed that incorporating the BERT model achieves outstanding performance and outperforms other models for an impressive sarcasm classification with a state-of-the-art F1 score of 98%. The outcomes revealed that the F1 scores for SVM, BiSLTM, and RNN are 93%, 95.05%, and 95.52%, respectively. Our experiment on the News Headlines dataset demonstrates that incorporating Distil-Bert to process the word vector enhances the performance of RNN, and BiLSTM notably improves their accuracy. The accuracy of the BiLSTM and RNN models when incorporating FT-IDT, Word2Vec, and GLoVe embeddings scored 93.9% and 93.8%, respectively. In contrast, these scores increased to 95.05% and 95.52% when these models incorporated Distil-Bert for text embedding. This augmentation can be recognized to the capability of Distil-Bert to acquire contextual information and semantic relationships between words, thereby enriching the word vector representation.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Evaluation of a Customer Development simulator using large language model-based multi agent system and synthetic respondents
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Lois Abigail To, Zachary Matthew Alabastro, Joseph Benjamin Ilagan
Abstract - Customer development (CD) is a Lean Startup (LS) methodology for startups to validate their business hypotheses and refine their business model based on customer feedback. This paper proposes designing a large language model-based multi-agent system (LLM MAS) to enhance the customer development process by simulating customer feedback. Using LLMs’ natural language understanding (NLU) and synthetic multi-agent capabilities, startups can conduct faster validation while obtaining preliminary insights that may help refine their business model before engaging with the real market. The study presents a model in which the LLM MAS simulates customer discovery interactions between a startup founder and potential customers, together with design considerations to ensure real-world accuracy and alignment with CD. If carefully designed and implemented, the model may serve as a useful co-pilot that accelerates the customer development process.
Paper Presenters
avatar for Lois Abigail To

Lois Abigail To

Philippines
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

Improving customer service in Ghana's banking industry utilizing ChatGPT to better understand customer sentiments
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Prince Kelvin Owusu, George Oppong Ampong, Joseph Akwetey Djossou, Gibson Afriyie Owusu, Thomas Henaku, Bless Ababio, Jean Michel Koffel
Abstract - In today's dynamic digital landscape, understanding customer opinions and sentiments has become paramount for businesses striving to maintain competitiveness and foster customer loyalty. However, the banking sector in Ghana faces challenges in effectively harnessing innovative technologies to grasp and respond to customer sentiments. This study aims to address this gap by investigating the application of ChatGPT technology within Ghanaian banks to augment customer service and refine sentiment analysis in real-time. Employing a mixed-method approach, the study engaged (40) representatives including IT specialists, data analysts, and customer service managers from (4) banks in Ghana through interviews. Additionally, (160) customers, (40) from each bank, participated in a survey. The findings revealed a significant misalignment between customer expectations and current service provisions. To bridge this gap, the integration of ChatGPT technology is proposed, offering enhanced sentiment analysis capabilities. This approach holds promise for elevating customer satisfaction and fostering loyalty within Ghana's competitive banking landscape.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

K-NEAREST NEIGBOUR CLASSIFICATION ACCURACY APPROACH USING WEIGHTED DISTANCE METRICS AND THREE WAY DECISIONS
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Japheth Otieno Ondiek, Kennedy Ogada, Tobias Mwalili
Abstract - This experiment models the implementation of distance metrics and three-way decisions for K-Nearest Neighbor classification (KNN). KNN as a machine learning method has inherent classification deficits due to high computing power, outliers and the curse of dimensionality. Many researchers have experimented and found that a combination of various algorithmic methods can lead to better results in prediction and forecasting fields. In this experimentation, we used the combination and strengths of the Euclidean metric distance to develop and evaluate computing query distance for nearest neighbors using weighted three-way decision to model a highly adaptable and accurate KNN technique for classification. The implementation is based on experimental design method to ascertain the improved computed Euclidean distance and weighted three-way decisions classification to achieve better computing power and predictability through classification in the KNN model. Our experimental results revealed that distance metrics significantly affects the performance of KNN classifier through the choice of K-Values. We found that K-Value on the applied datasets tolerate noise levels to ascertain degree while some distance metrics are less affected by the noise levels. This experiment primarily focused on the findings that best K-value from distance metrics measure guarantees three way KNN classification accuracy and performance. The combination of best distance metrics and three-way decision model for KNN classification algorithm has shown improved performance as compared with other conventional algorithm set-ups making in more ideal for classification in the context of this experiment. It outperforms KNN, ANN, DT, NB and the SVM from the crop yielding datasets applied in the experiment.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room A London, United Kingdom

11:45am GMT

An Exploration of the Application of Digital Twins for Healthcare
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Mmapula Rampedi, Funmi Adebesin
Abstract - The healthcare sector has generally been reluctant to adopt digital technologies. However, the COVID-19 pandemic pushed the industry to accelerate its digital transformation. Digital twins, a virtual replica of human organs or the entire human body, is revolutionizing healthcare and the management of healthcare resources. Digital twins can improve the accuracy of patients’ diagnoses through access to their virtual replica data. This enables healthcare professionals to make informed decisions about patients’ conditions and treatment options. This paper presents the results of a systematic literature review that investigated how digital twins are being utilized in the healthcare sector. A total of 6,714 papers published between 2019 and April 2024 were retrieved from four databases using specific search terms. A screening process based on inclusion and exclusion criteria resulted in a final set of 34 studies that were analyzed. The qualitative content analysis of the 34 studies resulted in the identification of five themes namely; (i) the technologies that are integrated into digital twins; (ii) the medical specialties where digital twins are being used; (iii) the different application areas of digital twins in healthcare; (iv) the benefits of the application of digital twins in healthcare and; (v) the challenges associated with the use of digital twins in healthcare. The outcome of the study showcased the potential for the adoption of digital twins to revolutionize healthcare service delivery by mapping the medical specialties of use to the different application areas. The study also highlights the benefits and challenges associated with the adoption of digital twins in the healthcare sector.
Paper Presenters
avatar for Mmapula Rampedi

Mmapula Rampedi

South Africa
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

BrainDetective: An Advanced Deep Learning Application for Early Detection, Segmentation and Classification of Brain Tumours Using MRI Images
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Nazli Tokatli, Mucahit Bayram, Hatice Ogur, Yusuf Kilic, Vesile Han, Kutay Can Batur, Halis Altun
Abstract - This study aims to create deep learning models for the early identification and classification of brain tumours. Models like U-Net, DAU-Net, DAU-Net 3D, and SGANet have been used to evaluate brain MRI images accurately. Magnetic resonance imaging (MRI) is the most commonly used method in brain tumour diagnosis, but it is a complicated procedure due to the brain’s complex structure. This study looked into the ability of deep learning architectures to increase the accuracy of brain tumour diagnosis. We used the BraTS 2020 dataset to segment and classify brain tumours. The U-Net model designed for the project achieved an accuracy rate of 97% with a loss of 47%, DAU-Net reached 90% accuracy with a loss of 33%, DAU-Net 3D achieved 99% accuracy with a loss of 35%, and SGANet achieved 99% accuracy with a loss of 20%, all demonstrating effective outcomes. These findings aim to improve patient care quality by speeding up medical diagnosis processes using computer-aided technology. Doctors can detect 3D tumours from MRI pictures using software developed as part of the research. The work packages correctly handled project management throughout the study’s data collection, model creation, and evaluation stages. Regarding brain tumour segmentation, 3D U-Net architecture with multi-head attention mechanisms provides doctors with the best tools for planning surgery and giving each patient the best treatment options. The user-friendly Turkish interface enables simple MRI picture uploads and quick, understandable findings.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Elicitation and Prioritisation of Requirements for a Clinical Decision Support System for Gait-related Diseases in Resource-limited Settings
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Radford Burger, Olawande Daramola
Abstract - Clinical Decision Support Systems (CDSS) have the potential to significantly improve healthcare quality in resource-limited settings (RLS). Despite evidence supporting the effectiveness of CDSS, their adoption and implementation rates remain low in RLS due to low levels of computer literacy among health workers, fragmented and unreliable infrastructure, and technical challenges. A thorough understanding of requirements is critical for the design of CDSS, which will be relevant to RLS. This paper explores the elicitation and prioritisation of requirements of a CDSS tailored to gait-related diseases in RLS. To do this, we conducted a qualitative literature analysis to identify potential requirements. After that, the requirements were presented to gait analysis experts for revision and prioritisation using the MoSCoW requirements prioritisation technique. The analysis of the results of the prioritisation process shows that for the functional requirements, 59.1% are fundamental and essential (Must Have), 36.3% are important but not fundamental (Should Have), 4.5% are negotiable requirements that are nice-to-have, but not important or fundamental (Could Have). All the non-functional requirements (100%) that pertain to usability and security were considered fundamental and essential (Must Have). This study provides a solid foundation for understanding the requirements of CDSS that are tailored to gait-related diseases in RLS. It also provides a guide for software developers and re-searchers on the design choices regarding the development of CDSS for RLS.
Paper Presenters
avatar for Radford Burger

Radford Burger

South Africa
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Enhancing Cybersecurity through Blockchain Technology
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Omar Ahmed Abdulkader, Bandar Ali Alrami Al Ghadmi, Muhammad Jawad Ikram
Abstract - In an era characterized by escalating digital threats, cybersecurity has emerged as a paramount concern for individuals and organizations globally. Traditional security measures, often reliant on centralized systems, face significant challenges in combating increasingly sophisticated cyberattacks, leading to substantial data breaches, financial losses, and erosion of trust. This paper investigates the transformative potential of blockchain technology as a robust solution to enhance cybersecurity frameworks. By leveraging the core principles of blockchain—decentralization, transparency, and immutability—this study highlights how blockchain can address critical cybersecurity challenges. For instance, the use of blockchain for data integrity ensures that information remains unaltered and verifiable, significantly reducing the risk of tampering. Furthermore, decentralized identity management systems can provide enhanced security against identity theft and phishing attacks, allowing users to maintain control over their personal information. Through a review of current applications and case studies, this paper illustrates successful implementations of blockchain in various sectors, including finance, healthcare, and supply chain management. Notable results include a reported 30% reduction in fraud rates within financial transactions utilizing blockchain technology and a marked improvement in incident response times due to the transparency and traceability offered by blockchain solutions. Despite its promising applications, this paper also addresses existing challenges, such as scalability issues that can hinder transaction speed, regulatory concerns that complicate implementation, and technical complexities that require specialized knowledge. These barriers pose significant obstacles to the widespread adoption of blockchain in cybersecurity. In conclusion, this paper emphasizes the need for further research and development to overcome these challenges and optimize the integration of blockchain within cybersecurity frameworks. By doing so, we can foster a safer digital environment and enhance resilience against the evolving landscape of cyber threats.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Exploring the Cultural Engagement Digital Model in the AlmeidAR Prototype A Study of User Preferences
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Catia Silva, Nelson Zagalo, Mario Vairinhos
Abstract - The preservation of cultural heritage, crucial for maintaining cultural identity, is increasingly threatened by natural degradation and socio-economic changes. Cultural tourism, supported by information and communication technologies, has become a key strategy for sustaining and promoting heritage sites. However, research on the most effective digital elements for amplifying tourist engagement remains limited. To address this gap, the present study explored the use of the Cultural Engagement Digital Model, which integrates participatory activities through game, narrative, and creativity elements, to enhance visitor engagement at cultural sites. The study focused on designing and testing three prototypes for Almeida, a historical village in in Guarda, Portugal, involving both visitors and interaction design experts to evaluate user preferences regarding the proposed activities. The findings of this study indicate that activities aligned with participatory dimensions can effectively engage users. These results help to solidify the model as a valuable instrument for designing mobile applications capable of promoting tourist engagement.
Paper Presenters
avatar for Catia Silva

Catia Silva

Portugal
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

Exploring the Use of Virtual Reminiscence in Pseudodementia A Single-Case Pilot Study
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Juliana Silva, Pedro Reisinho, Rui Raposo, Oscar Ribeiro, Nelson Zagalo
Abstract - As global life expectancy rises and the population of older adults in-creases, a higher prevalence of age-related diseases, such as dementia, is being observed. However, dementia-like symptoms are not exclusively caused by neurodegenerative conditions; pseudodementia, associated with late-life depression, can mimic the symptoms of dementia but may be potentially reversible with appropriate interventions. Despite this, individuals with pseudodementia still have a higher risk of progressing to neurodegenerative dementia. To counteract this possibility and aid in symptom reversal, non-pharmacological interventions may be a potential treatment. The present case study explored the feasibility of promoting storytelling through virtual reminiscence therapy in an older adult with pseudodementia, while also assessing the level of technological acceptance. The intervention included two sessions: one using a digital memory album and an-other utilizing 360º videos of personally significant locations. The results support the viability of using virtual reality as a therapeutic instrument to stimulate reminiscence and promote storytelling with a manageable learning curve and without inducing symptoms of cybersickness.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room B London, United Kingdom

11:45am GMT

A Comprehensive Review and Comparative Analysis of Photogrammetry Software Tools
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Omar Hamid, Homaiza Saud Ahmad, Ahmed Albayah, Fatima Dakalbab, Manar Abu Talib, Qassim Nasir
Abstract - The science of photogrammetry has been developing rapidly in recent years. With the rise of tools adopting this science and the advancement of computer vision technologies, the potential of such software is being acknowledged by researchers and integrated by market professionals into various fields. To cope with the rapid changes and expanding range of photogrammetry tools, a methodology was developed to identify the most widely adopted software tools, whether open-source or commercial, by the research community and market professionals. This resulted in the identification of 37 tools for which we developed a comprehensive review and presented our findings through visualizations such as pie charts and graphs. Furthermore, a comparison between the tools was carried out based on seven different attributes describing them, in order to assist professionals and individuals in picking software for specific use cases.
Paper Presenters
avatar for Omar Hamid

Omar Hamid

United Arab Emirates
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

A Conceptual Framework to Reveal Privacy Concerns in Smart Tourism
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Mona Kherees, Karen Renaud, Dania Aljeaid
Abstract - Smart Tourism is the most rapidly expanding economic sector, with data serving as the foundation of all Smart Tourism operations when travelers participate in various tailored travel services before, during, and after their journeys. The massive volume of data collected through various Smart Tourism Technologies raises tourists’ concerns. They might adopt privacy-preserving behaviors, like restricting sharing, fabricating data, or refusing to disclose requested information. Consequently, service providers manipulate users into disclosing personal data by employing persuasive marketing techniques based on Cialdini’s principles. This research aimed to investigate how the persuasion strategies of Cialdini employed by tourism organizations or service providers influence privacy concerns and users’ willingness to share personal information. A mixed-methods approach, incorporating expert reviews, was utilized to propose and validate a framework based on the Antecedents-Privacy Concerns-Outcome (APCO) model.
Paper Presenters
avatar for Mona Kherees

Mona Kherees

Saudi Arabia
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

A Framework to Improve Project Success Combining Knowledge Management with Project Management
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Otshepeng Lethabo Malebye, Tevin Moodley
Abstract - This paper explores integrating Knowledge Management principles within Project Management frameworks to address critical challenges project teams face, such as diminishing individual experience and employees applying their knowledge to the projects in which they are a part. This paper identifies common problems encountered in knowledge sharing, such as tacit knowledge externalisation and documentation within project environments, by exploring the KM principles and their relevance to project success. A proposed solution is presented by looking at existing systems, such as DocuWare and frameworks, Knowledge Management, and Project Management. This paper introduces a framework to demonstrate the significance of employing systematic processes for identifying, capturing, sharing, and applying knowledge within project teams. It utilises techniques such as interviews, post-project reviews, communities of practice, and training. By using the integrated approach, the proposed solution aims to solve knowledge silos, facilitate tacit knowledge externalisation, and improve knowledge documentation.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

A Multi-Channel Convolutional Neural Networks with Bidirectional LSTM : An Investigation into Social Network for the Identification of Fake News
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Ahamed Nishath S, Murugeswari R
Abstract - Researchers in the field of artificial intelligence are increasingly interested in exploring how to spot and counteract the spread of fake news. When compared to machine learning approaches, deep learning methods are superior in terms of their ability to reliably identify instances of false news. This study analyses the efficacy of various neural network topologies in the classification of news items into two distinct categories: false and real. This work takes into a hybrid model that merges both CNN and RNN layers incorporate with multi-channel mechanism, Which is the most complex model. When determining model’s overall performance, criteria such as accuracy, precision, and recall rates are taken into consideration. According to the findings, the hybrid model is able to efficiently attain a high degree of accuracy, particularly 99.16% of the target accuracy. The aforementioned results highlight the adaptability of various neural network designs in the context of distinguishing between real and false news, hence revealing key insights that have the potential to be implemented in practical scenarios involving the verification of information and the evaluation of its validity.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Evaluation study of an adaptive appointment booking system
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Massimo Carlini, Giuseppina Anatriello, Elisabetta Cicchiello
Abstract - The modern business context and the amount of data available to companies and organizations has made decision-making processes even more com-plex and articulated. This pushes companies to provide a better product or service for customers, reasoning in terms of quality, flexibility and responsiveness to their requests and needs. In this context, the concepts of customer centricity and satisfaction are placed, or the need for companies to try to satisfy demand by offering efficient and quality treatment aimed at satisfying customer needs based on a deep and solid knowledge of them. This paper reports on the activities carried out by Anas S.p.A., by Customer Ser-vice, over the last few years, to improve the Digital Customer Experience, making available to customers the knowledge and experience acquired over the years. The objective, in terms of Customer Centricity, was to put the customer at the center of the offer, providing them with more modern, innovative, intelligent and efficient dialogue tools.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Logical data model for key-value databases?
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Aniko Vagner
Abstract - NoSQL databases are grouped into many categories, one of them is the key-value databases. Our goal is to examine whether a system-independent key-value logical model exists. The idea came from the Redis database, which has the opaque key-value type named string, but it supports lists, hashes, sets, sorted sets, etc. If we compare them to the document databases storing JSON documents, they can have a system-independent logical model. We gathered databases said to fall into the key-value category and read their documentation considering the stored data structures. We found many subcategories under the key-value category. We found that the clean key-value databases with buckets can have a system-independent database model where the buckets collect the key-value pair, and the model is so easy. We could not identify a system independent logical model for the rest subcategories. Additionally, we recognised some viewpoints on which the data model of the key-value databases can be examined. Altogether, considering all subcategories we cannot speak about a system-independent logical data model for key value databases.
Paper Presenters
avatar for Aniko Vagner
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room C London, United Kingdom

11:45am GMT

Empowering Pathways: The Impact of Career Exploration and Self-Efficacy on Student Adaptability Through Career Calling and STARA Awareness
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Helvira Maharani Tresnadi, Rannie Oges Pebina, Permata Chandra Lagitha, Nurul Sukma Lestari
Abstract - This research aims to analyze the relationships between career calling, adaptability, and awareness of STARA technology to provide insights into career development during this critical transition phase. The methodology employed in this research is quantitative, with data collected through online questionnaire surveys. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) and Smart PLS software. The participants are students in Jakarta, with 413 respondents completing the survey. The findings indicate that both career exploration and self-efficacy have a positive influence on career adaptability. Furthermore, career exploration and self-efficacy significantly and positively affect career calling, while career calling positively affects career adaptability. The results also indicate that STARA Awareness reduces the influence of career calling on career adaptability, although the findings remain significant. The mediating variable demonstrates a positive and significant effect on the relationship between career exploration, self-efficacy, and career adaptability. The novelty of this research is that it examines career calling in school children, which is still rarely studied compared to employees, to help students recognize their potential and interests early on. For future research, it is recommended to investigate variables within a broader scope at the national and international levels.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Evaluation of deep learning techniques for non-destructive test in situ
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - ThiTuyetNga Phu, HongGiang Nguyen
Abstract - Inspecting the compressive strength of buildings' concrete is essential for ensuring the safety of households. This paper examined the study samplers collected using the nondestructive testing (NDT) method combined with Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests to check the beams of some apartments over 30 years old. Firstly, research samples were deployed to analyze the level of data variation using the exploratory data analysis (EDA) method to assess the reliability and correlation of data samples. Next, the study focused on the prediction of concrete compressive strength deploying five functions of activation (AF) (tanhLU, tanh, leakyLU, reLU, and sigmoid) by using two deep learning models as long short-term memory (LSTM) and gated recurrent unit (GRU). Lastly, the experimental results showed that the GRU model combined with two kinds of hybrid AFs gave a fairly accurate prediction level; in contrast, the remaining AF showed acceptable results.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Hardware Trojan Detection Using XGBoost Classifier with Focal Loss in IoT Integrated Devices
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Prathyush Kiran Holla, Manish M, Purvi Hande, Akshay Anand, Nirmala Devi M
Abstract - Integrated Circuits (IC) allows attackers to insert malicious implants called Hardware Trojans (HT). These Trojans leak information or alter circuit functionality. This threat is particularly critical in IoT devices, where compromised hardware can lead to drastic consequences across networks potentially exposing entire systems to data loss. Over the past decade, numerous Hardware Trojan Detection (HTD) methods have been developed which is crucial for securing IoT ecosystems, where detecting hardware-level threats early can prevent cascade failures. Current HTD techniques still face challenges with detection accuracy, class imbalance handling and high false positive/negative rates. We propose a HTD method using XGBoost, enhanced with focal loss to better handle class imbalance. XGBoost is combined with both graph-based and structural features to achieve higher accuracy compared to using each feature type individually. This approach is particularly valuable for IoT applications, where interconnected systems require robust detection methods. The proposed model, evaluated on an extensive dataset comprising of 41 combinational and sequential benchmark circuits, achieves an impressive accuracy of 98.85%, demonstrating superior performance in HT detection across diverse circuit architectures. Such high accuracy is essential for IoT deployments where false positives can trigger unnecessary disruptions across connected systems, and false negatives can leave critical infrastructure vulnerable to attacks.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Method for Estimating the Shape and Boundaries of the Uncertainty Region in Aircraft Positioning Using a Network of Optical-Electronic Stations
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Andriy Tevjashev, Oleksii Haluza, Dmytro Kostaryev, Anton Paramonov, Natalia Sizova
Abstract - The study focuses on estimating the accuracy of aircraft positioning using an infocommunication network of optical-electronic stations (OES). The problem addressed is the numerical estimation of the shape and boundaries of the region where the aircraft is located, with a given probability, at any fixed time during video surveillance in optical and infrared frequency ranges. The method departs from the traditional assumption of normal distribution for random errors in aircraft location estimates and employs Chebyshev's inequality to construct upper bounds for the uncertainty region. It is shown that the dispersion ellipsoid, often used to estimate the metrological characteristics of OES, is a rough approximation of the actual region where the aircraft is located with a given probability. The following results were obtained: – a method for constructing the actual uncertainty region of an aircraft’s location, based on the statistical properties of random errors in video surveillance from each OES and their relative spatial arrangement to the aircraft at each surveillance moment; – a software implementation of the numerical method for constructing and visualizing upper estimates of the shape and boundaries of the uncertainty region in aircraft positioning, using the OES network for trajectory measurements.
Paper Presenters
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Reflections on the Application of Immersive Technologies to Actively Mediate Visiting Experience in the Context of Museum Learning
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Qian Jiang, Kin Wai Michael Siu, Jiannong Cao
Abstract - Immersive technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), are widely used in exhibitions to engage audiences. This study examines immersive technologies in the context of museum learning with a focus on exhibitions. This study screened and analyzed 104 research papers in this scope closely related to the topic of immersive technologies and museums, which were selected based on search results for four keywords-human behavior, immersive technologies, exhibitions, and embedded experiences-to clarify the impact of immersive technologies on visitor behavior from existing exhibition themes. We conceptualized immersive technologies and categorized the literature according to theme and technology to clarify the relationship between immersive technology applications and exhibition topics. Existing research identifies a positive correlation between immersive technology and positive visitor experiences; however, there is less research on immersive technology and museum learning for special populations, and assessment tools for evaluating the effectiveness of technological application in this context have yet to be tested. The method of co-occurrence is used to analyze what factors need to be considered for the application of immersive technologies in the context of museum learning. Ultimately, a framework for immersive technological application is summarized.
Paper Presenters
avatar for Qian Jiang

Qian Jiang

Hong Kong
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

11:45am GMT

Understanding the Role of Artificial Intelligence Algorithms in Hiring through Professional Social Media Platforms
Friday February 21, 2025 11:45am - 1:15pm GMT
Authors - Unaizah Mahomed, Machdel Matthee
Abstract - The use of Professional Social Media Platforms (PSMPs) has become more popular in recent years. As COVID-19 spread globally the world was forced to fast-track digitalisation, remote and hybrid working models as well as the need for online hiring. This systematic literature review aims to give insight into understanding the role of artificial intelligence (AI) algorithms in professional social media platforms as well as gauge a deeper understanding for the need of these AI algorithms. This systematic literature review incorporates findings from previously published peer-reviewed literature to understand how AI-driven systems are used to improve hiring through professional social media platforms. The contents of this review address benefits related to hiring that include but is not limited to, the applications of AI algorithms in PSMPs, candidate screen and sourcing, job matching, and efficiency, as well as some concerns such as algorithmic bias, user privacy, regulations and ethical considerations. Significant effects on stakeholders have also been addressed within this review as well as the gaps within the research.
Paper Presenters
avatar for Unaizah Mahomed

Unaizah Mahomed

South Africa
Friday February 21, 2025 11:45am - 1:15pm GMT
Virtual Room D London, United Kingdom

1:15pm GMT

Session Chair Remarks
Friday February 21, 2025 1:15pm - 1:17pm GMT
Friday February 21, 2025 1:15pm - 1:17pm GMT
Virtual Room A London, United Kingdom

1:15pm GMT

Session Chair Remarks
Friday February 21, 2025 1:15pm - 1:17pm GMT
Friday February 21, 2025 1:15pm - 1:17pm GMT
Virtual Room B London, United Kingdom

1:15pm GMT

Session Chair Remarks
Friday February 21, 2025 1:15pm - 1:17pm GMT
Friday February 21, 2025 1:15pm - 1:17pm GMT
Virtual Room C London, United Kingdom

1:15pm GMT

Session Chair Remarks
Friday February 21, 2025 1:15pm - 1:17pm GMT
Friday February 21, 2025 1:15pm - 1:17pm GMT
Virtual Room D London, United Kingdom

1:17pm GMT

Closing Remarks
Friday February 21, 2025 1:17pm - 1:20pm GMT
Friday February 21, 2025 1:17pm - 1:20pm GMT
Virtual Room A London, United Kingdom

1:17pm GMT

Closing Remarks
Friday February 21, 2025 1:17pm - 1:20pm GMT
Friday February 21, 2025 1:17pm - 1:20pm GMT
Virtual Room B London, United Kingdom

1:17pm GMT

Closing Remarks
Friday February 21, 2025 1:17pm - 1:20pm GMT
Friday February 21, 2025 1:17pm - 1:20pm GMT
Virtual Room C London, United Kingdom

1:17pm GMT

Closing Remarks
Friday February 21, 2025 1:17pm - 1:20pm GMT
Friday February 21, 2025 1:17pm - 1:20pm GMT
Virtual Room D London, United Kingdom

1:58pm GMT

Opening Remarks
Friday February 21, 2025 1:58pm - 2:00pm GMT
Friday February 21, 2025 1:58pm - 2:00pm GMT
Virtual Room A London, United Kingdom

1:58pm GMT

Opening Remarks
Friday February 21, 2025 1:58pm - 2:00pm GMT
Friday February 21, 2025 1:58pm - 2:00pm GMT
Virtual Room B London, United Kingdom

1:58pm GMT

Opening Remarks
Friday February 21, 2025 1:58pm - 2:00pm GMT
Friday February 21, 2025 1:58pm - 2:00pm GMT
Virtual Room C London, United Kingdom

1:58pm GMT

Opening Remarks
Friday February 21, 2025 1:58pm - 2:00pm GMT
Friday February 21, 2025 1:58pm - 2:00pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

A Reduced Order Modeling for Radiation Source Identification and Localization in Incomplete Sensor Data Scenarios
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Indika Udagedara, Brian Helenbrook, Aaron Luttman
Abstract - This paper presents a reduced order modeling (ROM) approach for radiation source identification and localization using data from a limited number of sensors. The proposed ROM method comprises two primary steps: offline and online. In the offline phase, a spatial-energetic basis representing the radiation field for various source compositions and positions is constructed. This is achieved using a stochastic approach based on principal component analysis and maximum likelihood estimation. The online step then leverages these basis functions for determining the complete radiation field from limited data collected from only a few detectors. The parameters are estimated using Bayes rule with a Gaussian prior. The effectiveness of the ROM approach is demonstrated on a simplified model problem using noisy data from a limited number of sensors. The impact of noise on the model’s performance is analyzed, providing insights into its robustness. Furthermore, the approach was extended to real-world radiation detection scenarios, demonstrating that these techniques can be used to localize and identify the energy spectra of mixed radiation sources, composed of several individual sources, from noisy sensor data collected at limited locations.
Paper Presenters
avatar for Indika Udagedara

Indika Udagedara

United States
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Addressing Overfitting in Imbalanced Dataset for MS Progression Prediction
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Shima Pilehvari, Wei Peng, Yasser Morgan, Mohammad Ali Sahraian, Sharareh Eskandarieh
Abstract - Overfitting is a common problem during model training, particularly for binary medical datasets with class imbalance. This research specifically addresses this issue in predicting Multiple Sclerosis (MS) progression, with the primary goal of improving model accuracy and reliability. By investigating various data resampling techniques, ensemble methods, feature extraction, and model regularization, the study thoroughly evaluates the effectiveness of these strategies in enhancing stability and performance for highly imbalanced datasets. Compared to prior studies, this research advances existing approaches by integrating Kernel Principal Component Analysis (KPCA), moderate under-sampling, Synthetic Minority Oversampling Technique (SMOTE), and post-processing techniques, including Youden’s J Statistic and manual threshold adjustments. This comprehensive strategy significantly reduced overfitting while improving the generalization of models, particularly the Multilayer Perceptron (MLP), which achieved an Area Under the Curve (AUC) of 0.98—outperforming previous models in similar applications. These findings establish important best practices for developing robust prognostic models for MS progression and underscore the importance of tailored solutions in complex medical prediction tasks.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

An Adaptive Hypermedia Approach in Mental Health Assessments for High School Students
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Ain Nadhira Mohd Taib, Fauziah Zainuddin, M. Rahmah
Abstract - This paper presents AdaptiCare4U, an interactive mental health assessment in high school settings. By integrating adaptive technique with an establish mental health assessment instrument in a user-friendly format, Adap-tiCare4U improves the experience in answering mental health assessment. Through expert review validation technique, AdaptiCare4U demonstrates high effectiveness in accessibility, ease of use, and practical value with mean scores of 5, 4.2, and 4.4 respectively. Additionally, students’ perception further supports the tool’s usability, with positive feedback highlighting its engaging interface, use of multimedia elements, and stress-reducing design. A favorable usability rating from both students and experts makes AdaptiCare4U a promising tool for aiding counselors in conducting efficient mental health assessments.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Beyond Limits: Redefining Serverless Architecture Bridging AI, Caching, and Quantum Computing
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Aayush Kulkarni, Mangesh bedekar, Shamla Mantri
Abstract - This paper proposes a novel serverless computing model that addresses critical challenges in current architectures, namely cold start latency, resource inefficiency, and scalability limitations. The research integrates advanced caching mechanisms, intelligent load balancing, and quantum computing techniques to enhance serverless platform performance. Advanced distributed caching with coherence protocols is implemented to mitigate cold start issues. An AI-driven load balancer dynamically allocates resources based on real-time metrics, optimizing resource utilization. The integration of quantum computing algorithms aims to accelerate specific serverless workloads. Simulations and comparative tests demonstrate significant improvements in latency reduction, cost efficiency, scalability, and throughput compared to traditional serverless models. While quantum integration remains largely theoretical, early results suggest potential for substantial performance gains in tasks like function lookups and complex data processing. This research contributes to the evolving landscape of cloud computing, offering insights into optimizing serverless architectures for future applications in edge computing, AI, and data-intensive fields. The proposed model sets a foundation for more efficient, responsive, and scalable cloud solutions.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Deep Learning-Based Intrusion Detection
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Nouha Arfaoui, Mohmed Boubakir, Jassem Torkani, Joel Indiana
Abstract - The increasing reliance on surveillance systems and the vast amounts of video data have created a growing need for automated systems to detect violent and aggressive behaviors in real-time. Manual video analysis is not only labor-intensive but also prone to errors, particularly in large-scale monitoring situations. Machine learning and deep learning have gained significant attention for their ability to enhance the detection accuracy and efficiency of violence in images and videos. Violence is a critical societal issue, occurring in public spaces, workplaces, and social environments, and is a leading cause of injury and death. While video surveillance is a key tool for monitoring such behaviors, manual monitoring remains inefficient and subject to human fatigue. Early ML methods relied on manual feature extraction, which limited their flexibility in dynamic scenarios. Ensemble techniques, including AdaBoost and Gradient Boosting, provided improvements but still required extensive feature selection. The introduction of deep learning, particularly Convolutional Neural Networks (CNNs), has enabled automatic feature learning, making them more effective in violence detection tasks. This study focuses on detecting violence and aggression in workplace settings by addressing key aspects such as violent actions, and aggressive objects, utilizing various deep learning algorithms to identify the most efficient model for each task.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

Resilient IoT Infrastructures: Machine Learning Approaches to Autonomous Anomaly Detection and Threat Neutralization
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Kalupahanage A. G. A, Bulathsinhala D.N, Herath H.M.S.D, Herath H.T.M.T, Shashika Lokuliyana, Deemantha Siriwardana
Abstract - The explosive growth of the Internet of Things (IoT) has had a substantial impact on daily life and businesses, allowing for real-time monitoring and decision-making. However, increased connectivity also brings higher security risks, such as botnet attacks and the need for stronger user authentication. This research explores how machine learning can enhance Internet of Things security by identifying abnormal activity, utilizing behavioral biometrics to secure cloud-based dashboards, and detecting botnet threats early. Researchers tested numerous machine learning methods, including K-Nearest Neighbors (KNN), Decision Trees, and Logistic Regression, on publicly available datasets. The Decision Tree model earned an impressive accuracy rate of 73% for anomaly identification, proving its supremacy in dealing with complex security risks. Research findings show the effectiveness of these strategies in enhancing the security and reliability of IoT devices. This study provides significant insights into the use of machine learning to protect Internet of Things devices while also addressing crucial concerns such as power consumption and privacy.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room A London, United Kingdom

2:00pm GMT

A Deep Learning Approach for Proactive Detection and Mitigation of Zero-Day Attacks in IoT Environment
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Franciskus Antonius Alijoyo, N Venkatramana, Omaia AlOmari, Shamim Ahmad Khan, B Kiran Bala
Abstract - The Internet of Things (IoT) is becoming a crucial component of many industries, from smart cities to healthcare, in today's networked world. IoT devices are becoming more and more susceptible to security risks, especially zero-day (0day) attacks, which take advantage of undiscovered flaws. The dynamic and dispersed nature of these systems makes it difficult to identify and mitigate these assaults in IoT contexts. This research focuses on a deep learning model that was created and put into use with Python software. It was made especially to do a detection job with great accuracy. The proposed Autoencoder (AE) with Attention Mechanism model demonstrates exceptional performance in detecting zero-day attacks, achieving an accuracy of 99.45%, precision of 98.56%, recall of 98.53%, and an F1 score of 98.21%. The involvement of the attention mechanism helps to focus on the most relevant features, enhancing its efficiency and reducing computational overhead, making it a promising solution for real-time security applications in IoT systems. Compared to previous methods, such as STL+SVM and AE+DNN, the proposed model significantly outperforms the methods. These results highlight its superior ability to identify anomalies with minimal false positives. Because of its resilience, the model is very good at making zero-attacks. The results demonstrate how deep learning may improve IoT systems' security posture by offering proactive, real-time protections against zero-day threats, resulting in safer and more robust IoT environments.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Development of Artificial Intelligent Driven Web Application for Detecting Maize Diseases
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Freedom Khubisa, Oludayo Olugbara
Abstract - This paper presents the development and evaluation of an artificial intelligent (AI) driven web application for detecting maize diseases. The AI application was designed according to the design science methodology to offer accurate and real-time detection of maize diseases through a user-friendly interface. The application used Flask framework and Python programming language, leveraging multiple libraries and Application Programming Interfaces (APIs) to handle aspects such as database, real-time communication, AI models, weather forecast data, and language translation. The application's AI model is a stacked ensemble of cutting-edge deep learning architectures. Technical performance testing was performed using GTmetrix metrics, and the results were remarkable. The WebQual4.0 framework was used to evaluate the application's usability, information quality and service interaction quality. The Cronbach’s alpha (α) reliability measure was applied to assess internal consistency for WebQual4.0, which yielded an acceptable reliability score of 0.6809. The usability analysis showed that users perceived the AI-driven web application as intuitive, with high scores computed for navigation and ease of use. The quality of information was rated positive with users appreciating the reliability and relevance of the maize disease detection results of the AI application. The service interaction indicated potential for enhancement, which is a solicitude also highlighted in qualitative user feed-back that will be considered for future improvement. The study findings generally indicated that our AI application has great potential to improve agricultural practices by providing early maize disease diagnostics and decisive information to aid maize farmers and enhance maize yields.
Paper Presenters
avatar for Freedom Khubisa

Freedom Khubisa

South Africa
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Machine Learning Approach for Liver Disease Prediction
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Abdulrahman S. Alenizi, Khamis A. Al-Karawi
Abstract - The liver is a vital organ responsible for numerous physiological functions in the human body. In recent years, the prevalence of liver diseases has risen significantly worldwide, mainly due to unhealthy lifestyle choices and excessive alcohol use. This illness is made worse by several hepatotoxic reasons. Obesity is the root cause of chronic liver disease. Obesity, undiagnosed viral hepatitis infections, alcohol consumption, increased risk of hemoptysis or hematemesis, renal or hepatic failure, jaundice, hepatic encephalopathy, and many other conditions can all contribute to chronic liver disease. Using machine learning for illness identification, hepatitis, an infection inflating liver tissue, has been thoroughly investigated. Numerous models are employed to diagnose illnesses, but limited research focuses on the connections between hepatitis symptoms. This research intends to examine chronic liver disease through machine learning predictions. It assesses the efficacy of multiple algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), and Decision Tree, by quantifying their accuracy, precision, recall, and F1 score. Experiments were performed on the dataset utilising these classifiers to evaluate their efficacy. The findings demonstrate that the Random Forest method attains the highest accuracy at 87.76%, surpassing other models in disease prediction. It also demonstrates superiority in precision, memory, and F1 score. Consequently, the study concludes that the Random Forest model is the most effective for predicting liver disease in its early stages.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Machine Learning for Anomaly Detection in Auditing and Financial Error Detection: Methods and Practical Applications at Thai Nguyen province
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Phuong Thao Nguyen
Abstract - In recent years, the application of machine learning (ML) in anomaly detection for auditing and financial error detection has garnered significant attention. Traditional auditing methods, often reliant on manual inspection, face challenges in accuracy and efficiency, especially when handling large datasets. This study explores the integration of ML techniques to enhance the detection of anomalies in financial data specific to Thai Nguyen Province, Vietnam. We evaluate multiple ML algorithms, including supervised models (logistic regression, support vector machines) and unsupervised models (k-means clustering, isolation forest, autoencoders), to identify unusual patterns and potential financial discrepancies. Using financial records and audit reports from Thai Nguyen, the models were trained and tested to assess their accuracy, precision, and robustness. Our findings demonstrate that ML models can effectively detect anomalies and improve error identification compared to traditional methods. This paper provides practical insights and applications for local auditors, highlighting ML’s potential to strengthen financial oversight and fraud prevention within Thai Nguyen. Future research directions are also proposed to enhance model interpretability and address unique challenges in Vietnamese financial contexts.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Parameter-Efficient Fine-Tuning of LLaMA 2 for the Kazakh Language: Advancing Low-Resource Language Models
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Aman Mussa, Madina Mansurova
Abstract - The rapid advancement of neural networks has revolutionized multiple domains, as evidenced by the 2024 Nobel Prizes in Physics and Chemistry, both awarded for contributions to neural networks. Large language models (LLMs), such as ChatGPT, have significantly reshaped AI interactions, gaining unprecedented growth and recognition. However, these models still face substantial challenges with low-resource languages like Kazakh, which accounts for less than 0.1% of online content. The scarcity of training data often results in unstable and inaccurate outputs. To address this issue, we present a novel Kazakh language dataset specifically designed for self-instruct fine-tuning of LLMs, comprising 50,000 diverse instructions from internet sources and textbooks. Using Low-Rank Adaptation (LoRa), a parameter-efficient fine-tuning technique, we successfully fine-tuned the LLaMA 2 model on this dataset. Experimental results demonstrate improvements in the model’s ability to comprehend and generate Kazakh text, despite the absence of established benchmarks. This research underscores the potential of large-scale models to bridge the performance gap in low-resource languages and highlights the importance of curated datasets in advancing AI-driven technologies for underrepresented linguistic communities. Future work will focus on developing robust benchmarking standards to further evaluate and enhance these models.
Paper Presenters
avatar for Aman Mussa

Aman Mussa

Kazakhstan
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

Surface linguistic features for multiclass fake news detection in a multilingual context
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Eduardo Puraivan, Pablo Ormeno-Arriagada, Steffanie Kloss, Connie Cofre-Morales
Abstract - We are in the information age, but also in the era of disinformation, with millions of fake news items circulating daily. Various fields are working to identify and understand fake news. We focus on hybrid approaches combining machine learning and natural language processing, using surface linguistic features, which are independent of language and enable a multilingual approach. Many studies rely on binary classification, overlooking multiclass problems and class imbalance, often focusing only on English. We propose a methodology that applies surface linguistic features for multiclass fake news detection in a multilingual context. Experiments were conducted on two datasets, LIAR (English) and CLNews (Spanish), both imbalanced. Using Synthetic Minority Oversampling Technique (SMOTE), Random Oversampling (ROS), and Random Undersampling (RUS), we observed improved class detection. For example, in LIAR, the classification of the ‘false’ class improved by 43.38% using SMOTE with Adaptive Boosting. In CLNews, the ROS technique with Random Forest raised accuracy to 95%, representing a 158% relative improvement over the unbalanced scenario. These results highlight our approach’s effectiveness in addressing the problem of multiclass fake news detection in an imbalanced, multilingual context.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room B London, United Kingdom

2:00pm GMT

A Review of Privacy Risks of Third-Party Web Analytics
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Timi Heino, Sampsa Rauti, Sammani Rajapaksha, Panu Puhtila
Abstract - Today, web analytics services are widely used on modern websites. While their main selling point is to improve the user experience and return of investment, de facto it is to increase the profits of third-party service providers through the access to the harvested data. In this paper, we present the current state-of-the-art research on the use of web analytics tools, and what kind of privacy threats these applications pose for the website users. Our study was conducted as a literature review, where we focused on papers that described third-party analytics in detail and which discussed their relation to user privacy and the privacy challenges they pose. We focused specifically on papers dealing with the practical third-party analytics tools, such as Google Analytics or CrazyEgg. We review the application areas, purposes of use, and data items collected by web analytics tools, as well as privacy risks mentioned in the literature. Our results show that web analytics tools are used in ways which severely compromise user privacy in many areas. Practices such as collecting a wide variety of unnecessary data items, storing data for extended periods of time without a good reason and not informing users appropriately are common. In this study, we also give some recommendations to alleviate the situation.
Paper Presenters
avatar for Timi Heino

Timi Heino

Finland
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Empirical Evidence on the Reliability of a Scale for Measuring Computer Skills in Older Adults
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Abigail Gonzalez-Arriagada, Ruben Lopez-Leiva, Connie Cofre-Morales, Eduardo Puraivan
Abstract - The rapid advancement of information and communication technologies (ICT) has created a significant digital divide between older adults and younger generations. This divide affects the autonomy of older adults in a digitalized world. To address this issue, various initiatives have attempted to promote their digital skills, which requires reliable tools to measure them. However, assessing these competencies in this age group presents complex challenges, such as developing scales that accurately reflect the dimensions involved. In this study, we present empirical evidence on the reliability and adaptation of the Assessment of Computer-Related Skills (ACRS) scale. We translated the instrument into Spanish and added descriptors to optimize its application. The evaluation included 54 older adults in Chile (39 women and 15 men, aged 55 to 80) in an environment designed for individualized observation during the performance of specific digital tasks. The analyses revealed that the five dimensions of the instrument have high reliability, with Cronbach’s alpha values between 0.959 and 0.968. Six items were identified whose removal could slightly improve this indicator. Overall, the scale shows excellent internal consistency, with a G6 coefficient of 0.9994. These results confirm that, both at the level of each dimension and as a whole, the instrument demonstrates strong internal consistency, reinforcing its utility for assessing the intended competencies. An additional contribution of this work is the public availability of the data obtained, with the aim of encouraging future research in this area. Given the nature of the scale, which allows for the assessment of skills across various computer-related tasks, evidence of its high internal reliability constitutes a valuable resource for designing more inclusive educational programs specifically tailored to the needs of older adults in digital environments.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Evaluation of Software System based on Methodology Digital Forensics Investigation from Practical Point of View DFIP
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Svetlin Stefanov, Malinka Ivanova
Abstract - The advent of new technologies leads to a complexity of the cyber-crime landscape and scenes, which requires an adequate response from digital forensic investigators. To support their forensic activities, a number of models and methodologies have been developed, such as the methodology Digital Forensics Investigation from Practical Point of View DFIP, proposed by us in a previous work. In addition, there is an urgent need for a virtual environment that would organize and manage the activities of investigators related to communication, document exchange, preparation of computer expertise, teamwork, information delivery and training. In this context, a software system implementing the DFIP methodology has been developed, and the aim of the paper is to present the results of a study regarding the opinion and attitudes of forensic experts on the usefulness and role of the software system during the different phases of digital forensic investigation.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Investigating Third-Party Data Leaks and in Online Electronics Stores
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Timi Heino, Robin Carlsson, Panu Puhtila, Sammani Rajapaksha, Henna Lohi, Sampsa Rauti
Abstract - Electronics is one of the most popular product categories among consumers online. In this paper, we conduct a study on the thirdparty data leaks occurring in the websites of the most online electronics stores used by Finnish residents, as well as the amounts of third parties present at these websites. We studied the leaks by recording and analyzing the network traffic happening from the website while conducting actions at the website that the normal user does when purchasing the product. We also analyze dark patterns found in these websites’ cookie consent banners. Our results show that in 80% of the cases, the product name, product ID and price were leaked to third parties along with the data identifying the user. Almost all of the inspected websites used dark patterns in their cookie consent banners, and privacy policies often had severe deficiencies in informing the user of the extent of data collection.
Paper Presenters
avatar for Timi Heino

Timi Heino

Finland
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Lifestyles and Stress Management of Families in Confinement
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Luis E. Quito-Calle, Maria E. Barros-Ponton, Dalila M. Gonzalez-Gonzalez, Luis F. Guerrero-Vasquez, Jessica V. Quito-Calle
Abstract - The confinement of families, whether due to health emergencies or other quarantines, has caused lifestyle changes to cause changes in the behavior of population and cause stress among its members when facing confinement. Present study aimed to determine if there is an association between the lifestyles and parents’ coping with stress due to confinement due to the Health Emergency or quarantine due to COVID- 19. This study methodology was quantitative, descriptive, correlational and cross-sectional. Participants were made up of 75 representatives of Bilingual Educational Institute "Home and School" INEBHYE. Instruments used were Lifestyle Profile Questionnaire (PEPS-I, in Spanish) and Stress Coping Questionnaire (CAE, in Spanish) with which it was obtained as a result that a healthy lifestyle predominates because families have been facing their stress under problem solving, positive reassessment and religion in the face of confinement. As a conclusion, it is obtained that there is a statistically significant association between the subscales of coping with stress and families lifestyle, which would imply a change in lifestyle to face the stress caused by confinement due to COVID-19.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Principal Component Analysis and Machine Learning for Classification of Coffee Yield
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Vicente A. Pitogo, Cristopher C. Abalorio, Rolyn C. Daguil, Ryan O. Cuarez, Sandra T. Solis, Rex G. Parro
Abstract - The agricultural resources in the Philippines are essential for national food security and economic development with coffee being at its center. Moreover, recent data released by the Philippine Statistics Authority (PSA) show an increase in coffee production although there has been a worrying decline in pro-duction in Caraga region which grows over two thousand five hundred growers and has a huge area of land planted to coffee. The FarmVista project addressed this challenge through a data-driven approach by applying Principal Component Analysis (PCA) and various machine learning algorithms to classify and analyze coffee yield in Caraga. The study utilized a comprehensive dataset, the Coffee Farmers Enumerated Data, encompassing socio-demographic details, farming practices, and other influential factors. Gradient Boosting achieved the highest accuracy of 98.69%, with Random Forest closely following at 95.63%. These results highlight the effectiveness of advanced analytics and machine learning in improving coffee yield classification. By uncovering key patterns and factors affecting yield quality, this study provides valuable insights to optimize the coffee value chain in Caraga and addresses the region’s production challenges.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room C London, United Kingdom

2:00pm GMT

Analyzing E-Commerce Customer Complaints with Latent Semantic Analysis: a Case Study from Brazil
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Bruno Zaninotto, Carlos Eduardo Barbosa, Alice Fonseca Monteiro, Lucas Nobrega, Luiz Felipe Martinez, Matheus Argolo, Geraldo Xexeo, Jano Moreira de Souza
Abstract - The dynamic between buyers and sellers in the retail sector often leads to conflicts, necessitating a deeper understanding of customer complaints. The Internet is where customers can voice their opinions to influence purchasing decisions and shape company reputations. Brazil, recognized among the top 10 countries with the highest expectations for e-commerce growth worldwide in 2022, demonstrates a rapidly expanding market ready for exploration. This study addresses the problem by applying Latent Semantic Analysis (LSA) to analyze complaints about Americanas S.A., a large retail company on the Reclame Aqui platform, using the company as a case study for broader methodological application. Our findings reveal significant uniformity in complaints across Brazil, primarily concerning order processing, delivery, and product quality. These insights offer actionable intelligence for retailers to refine their Customer Relationship Management strategies and for the government to strengthen consumer protection policies, demonstrating the utility of LSA in improving customer satisfaction and trust in the retail landscape.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Cross-Language Approach for Quranic QA
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Islam Oshallah, Mohamed Basem, Ali Hamdi, Ammar Mohammed
Abstract - Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide. However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. Experimental results demonstrate that this cross-language approach significantly improves model performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), while DeBERTa-v3-Base excels in Recall@10 (0.50) and Precision@10 (0.24). These findings underscore the effectiveness of cross-language strategies in overcoming linguistic barriers and advancing Quranic QA systems.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Factors and prospects for the development of digital educational platforms in Uzbekistan
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Aziza Irmatova, Mukhabbatkhon Mirzakarimova, Dilafruz Iskandarova, Guli-ra'no Abdumalikova
Abstract - In the today, the development of digital education is playing an important role in radically changing the education system and making learning processes more innovative, interactive and convenient. In particular, digital platforms are the main tools that can change the educational process. Through these platforms, students have the opportunity to study lessons anywhere and at any time, without being limited to traditional classrooms. From this point of view, the development and implementation of digital educational platforms in educational institutions is one of the urgent issues, and the success of this process largely depends on the Internet coverage in the country, investments in digital infrastructure, and the impact of government policy. This article empirically analyzes the impact of Internet coverage, investments in digital infrastructure, and government policy on the implementation of digital educational platforms in Uzbekistan. The measurement of government policy was carried out by assessing the public's assessment of government policy.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Baraa Hikal, Ahmed Nasreldin, Ali Hamdi, Ammar Mohammed
Abstract - Hallucination detection in text generation remains an ongoing struggle for natural language processing (NLP) systems, frequently resulting in unreliable outputs in applications such as machine translation and definition modeling. Existing methods struggle with data scarcity and the limitations of unlabeled datasets, as highlighted by the SHROOM shared task at SemEval-2024. In this work, we propose a novel framework to address these challenges, introducing DeepSeek Few-shot Optimization to enhance weak label generation through iterative prompt engineering. We achieved high-quality annotations that considerably enhanced the performance of downstream models by restructuring data to align with instruct generative models. We further fine-tuned the Mistral-7B-Instruct-v0.3 model on these optimized annotations, enabling it to accurately detect hallucinations in resource-limited settings. Combining this fine-tuned model with ensemble learning strategies, our approach achieved 85.5% accuracy on the test set, setting a new benchmark for the SHROOM task. This study demonstrates the effectiveness of data restructuring, few-shot optimization, and fine-tuning in building scalable and robust hallucination detection frameworks for resource-constrained NLP systems.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Youssef Maklad, Fares Wael, Wael Elsersy, Ali Hamdi
Abstract - This paper presents a novel approach to evaluate the efficiency of a RAG-based agentic Large Language Model (LLM) architecture in network packet seed generation for network protocol fuzzing. Enhanced by chain-of-thought (COT) prompting techniques, the proposed approach focuses on the improvement of the seeds’ structural quality in order to guide protocol fuzzing frameworks through a wide exploration of the protocol state space. Our method leverages RAG and text embeddings in a two-stages. In the first stage, the agent dynamically refers to the Request For Comments (RFC) documents knowledge base for answering queries regarding the protocol Finite State Machine (FSM), then it iteratively reasons through the retrieved knowledge, for output refinement and proper seed placement. In the second stage, we evaluate the response structure quality of the agent’s output, based on metrics as BLEU, ROUGE, and Word Error Rate (WER) by comparing the generated packets against the ground truth packets. Our experiments demonstrate significant improvements of up to 18.19%, 14.81%, and 23.45% in BLEU, ROUGE, and WER, respectively, over baseline models. These results confirm the potential of such approach, improving LLM-based protocol fuzzing frameworks for the identification of hidden vulnerabilities.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

2:00pm GMT

XEMST: Revolutionizing Smart Medical Logistics with Advanced Humidity Prediction through Stacking Ensemble Models
Friday February 21, 2025 2:00pm - 3:30pm GMT
Authors - Tushar Vasudev, Surbhi Ranga, Sahil Sankhyan, Praveen Kumar, K V Uday, Varun Dutt
Abstract - To guarantee the safety and effectiveness of medical supplies like blood and vaccinations, careful environmental monitoring is necessary throughout transit. Even while real-time monitoring has advanced, current systems sometimes lack strong predictive ability to foresee unfavorable circumstances. The XGBoost Ensemble for Medical Supplies Transport (XEMST), a unique stacking ensemble model created to predict interior humidity levels during travel, is presented in this paper to fill this gap. By utilizing XGBoost's outstanding predictive fusion capabilities, the model incorporates predictions from fundamental machine learning methods, including Support Vector Machine, Random Forest, Decision Tree, and Linear Regression. XEMST outperformed individual models with a Root Mean Squared Error (RMSE) of 2.22% and an R2 score of 0.96 when tested across 17 different transit situations. By enabling prompt responses, these predictive insights protect medical supply quality from environmental hazards. This study demonstrates how sophisticated ensemble learning frameworks have the potential to transform intelligent healthcare logistics.
Paper Presenters
Friday February 21, 2025 2:00pm - 3:30pm GMT
Virtual Room D London, United Kingdom

3:30pm GMT

Session Chair Remarks
Friday February 21, 2025 3:30pm - 3:33pm GMT
Friday February 21, 2025 3:30pm - 3:33pm GMT
Virtual Room A London, United Kingdom

3:30pm GMT

Session Chair Remarks
Friday February 21, 2025 3:30pm - 3:33pm GMT
Friday February 21, 2025 3:30pm - 3:33pm GMT
Virtual Room B London, United Kingdom

3:30pm GMT

Session Chair Remarks
Friday February 21, 2025 3:30pm - 3:33pm GMT
Friday February 21, 2025 3:30pm - 3:33pm GMT
Virtual Room C London, United Kingdom

3:30pm GMT

Session Chair Remarks
Friday February 21, 2025 3:30pm - 3:33pm GMT
Friday February 21, 2025 3:30pm - 3:33pm GMT
Virtual Room D London, United Kingdom

3:33pm GMT

Closing Remarks
Friday February 21, 2025 3:33pm - 3:35pm GMT
Friday February 21, 2025 3:33pm - 3:35pm GMT
Virtual Room A London, United Kingdom

3:33pm GMT

Closing Remarks
Friday February 21, 2025 3:33pm - 3:35pm GMT
Friday February 21, 2025 3:33pm - 3:35pm GMT
Virtual Room B London, United Kingdom

3:33pm GMT

Closing Remarks
Friday February 21, 2025 3:33pm - 3:35pm GMT
Friday February 21, 2025 3:33pm - 3:35pm GMT
Virtual Room C London, United Kingdom

3:33pm GMT

Closing Remarks
Friday February 21, 2025 3:33pm - 3:35pm GMT
Friday February 21, 2025 3:33pm - 3:35pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

AgriPredict: Threat Assessment Model for Agricultural Management
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - A B Sagar, K Ramesh Babu, Syed Usman, Deepak Chenthati, E Kiran Kumar, Boppana Balaiah, PSD Praveen, G Allen Pramod
Abstract - Agricultural disasters, mostly ones caused by biological threats, pose severe threats to global food security and economic stability. Early detection and effective management are essential for mitigating these risks. In this research paper we propose a comprehensive disaster prediction and management framework integrating any of the resources like social networks or Internet of Things (IoT) for data collection. The model combines real-time data collection, risk assessment, and decision-making processes to forecast agricultural disasters and suggest mitigation strategies. The mathematical foundation of this model defines relationship between key variables, such as plant species, infestation agent species, tolerance levels, and infestation rates. The system relies on IoT or mobile-based social network agents for data collection at the ground level, to get precise and consistent information from diverse geographic regions. The model further includes a hierarchical risk assessment process that identifies, evaluates, and assesses risks based on predefined criteria, enabling informed decision-making for disaster mitigation. Multiplant species and multi-infestation agent interactions are also considered to capture the complexities of agricultural systems. The proposed framework provides a scalable approach to predicting and managing agricultural disasters, particularly targeting biological threats. By incorporating real-time data and dynamic decision-making mechanisms, the model considerably improves the resilience of agricultural systems against both localized and large-scale threats.
Paper Presenters
avatar for A B Sagar
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Analysis of vehicle traffic trends, using the Social Network X (Twitter), Case Study, Quito-Ecuador
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Herrera Nelson, Paul Francisco Baldeon Egas, Gomez-Torres Estevan, Sancho Jaime
Abstract - Quito, the capital of Ecuador, is the economic core of the country where commercial, administrative, and tourist activities are concentrated. With population growth, the city has undergone major transformations resulting in traffic congestion problems that affect health, cause delays in daily activities, and increase pollution levels among other inconveniences. Over time, important mobility initiatives have been implemented such as traffic control systems, monitoring, construction of peripheral roads, and the "peak and license plate" measure that restricts the use of vehicles during peak hours according to their license plate, a strategy also adopted in several Latin American countries. However, these actions have not been enough, and congestion continues to increase, causing discomfort to citizens. Given this situation, the implementation of a low-cost computer application has been proposed that allows identifying traffic situations in real time and making decisions to improve this problem using processed data from the social network Twitter and traffic records from the city of Quito.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Analyzing the Structure of Groupoids of order 3, 4, and 5 Using PCA
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Elissa Mollakuqe, Hasan Dag, Vesa Mollakuqe, Vesna Dimitrova
Abstract - Groupoids are algebraic structures, which generalize groups by allowing partial symmetries, and are useful in various fields, including topology, category theory, and algebraic geometry. Understanding the variance explained by Principal Component Analysis (PCA) components and the correlations among variables within groupoids can provide valuable insights into their structures and relationships. This study aims to explore the use of PCA as a dimensionality reduction technique to understand the variance explained by different components in the context of groupoids. Additionally, we examine the interrelationships among variables through a color-coded correlation matrix, facilitating insights into the structure and dependencies within groupoid datasets. The findings contribute to the broader understanding of data representation and analysis in mathematical and computational frameworks.
Paper Presenters
avatar for Vesa Mollakuqe

Vesa Mollakuqe

North Macedonia
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Berthing Vessels Against Wind Turbines In A Real Seastate
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Laurent BARTHELEMY
Abstract - In 2024 [7], the author proposed a calculation of weather criteria for vessel boarding against the ladder of an offshore wind turbine, based on a regular wave. However international guidelines [2] prescribe that "95% waves pass with no slip above 300mm (or one ladder rung)". In order to meet such acceptability criteria, it becomes necessary to investigate boarding under a real state, which is an irregular wave. The findings meet the results from other publications [6] [7]. The outcome then is to propose boarding optimisation strategies, compared to present professional practises. The purpose is to achieve less gas emissions, by minimising fuel consumption.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Breast Ultrasound Imaging Classification Using Federated Learning Techniques
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Amro Saleh, Nailah Al-Madi
Abstract - Machine learning (ML) enables valuable insights from data, but traditional ML approaches often require centralizing data, raising privacy and security concerns, especially in sensitive sectors like healthcare. Federated Learning (FL) offers a solution by allowing multiple clients to train models locally without sharing raw data, thus preserving privacy while enabling robust model training. This paper investigates using FL for classifying breast ultrasound images, a crucial task in breast cancer diagnosis. We apply a Convolutional Neural Network (CNN) classifier within an FL framework, evaluated through methods like FedAvg on platforms such as Flower and TensorFlow. The results show that FL achieves competitive accuracy compared to centralized models while ensuring data privacy, making it a promising approach for healthcare applications.
Paper Presenters
avatar for Amro Saleh
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Toward an Integrated Health Volunteering (IHV) Framework to Aid Decision-making in the Context of Hajj Pilgrimage
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Ahmed D. Alharthi, Mohammed M. Tounsi
Abstract - The Hajj pilgrimage represents one of the largest mass gatherings globally, posing substantial challenges in terms of health and safety management. Millions of pilgrims converge each year in Saudi Arabia to fulfil their religious obligations, underscoring the critical need to address the various health risks that may emerge during such a large-scale event. Health volunteering plays a pivotal role in delivering timely and high-quality medical services to pilgrims. This study introduces the Integrated Health Volunteering (IHV) framework, designed to enhance health and safety outcomes through an optimised, rapid response system. The IHV framework facilitates the coordinated deployment of healthcare professionals—including doctors, anaesthetists, pharmacists, and others—in critical medical emergencies such as cardiac arrest and severe haemorrhage. Central to this framework is the integration of advanced technologies, including Artificial Intelligence algorithms, to support health volunteers’ decision-making. The framework has been validated and subjected to accuracy assessments to ensure its efficacy in real-world situations, particularly in the context of mass gatherings like the Hajj.
Paper Presenters
avatar for Mohammed M. Tounsi

Mohammed M. Tounsi

Saudi Arabia
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room A London, United Kingdom

4:15pm GMT

Deep Learning Approaches in EEG Signal Classification for P300 Brain Speller Development: A Comprehensive Analysis
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Mariam Esmat, Mohamed Elgemeie, Mohamed Sokar, Heba Ali, Sahar Selim
Abstract - This paper explores the relationship between deep learning approaches and the intricate nature of EEG signals, focusing on the development of a P300 brain speller. The study uses an underutilized dataset to explore the classification of EEG signals and distinguishing features of "target" and "non-target" signals. The data processing adhered to current literature standards, and various deep learning methods, including Recurrent Neural Networks, Artificial Neural Networks, Transformers, and Linear Discriminant Analysis, were employed to classify processed EEG signals into target and non-target categories. The classification performance was evaluated using the area under the curve (AUC) score and accuracy. This research lays a foundation for future advancements in understanding and utilizing the human brain in neuroscience and technology.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Offline Reinforcement Learning Approaches for Safe and Effective Smart Grid Control
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Angel Peredo, Hector Lugo, Christian Narcia-Macias, Jose Espinoza, Daniel Masamba, Adan Gandarilla, Erik Enriquez, Dong-Chul Kim
Abstract - This paper explores the under-examined potential of offline reinforcement learning algorithms in the context of Smart Grids. While online methods, such as Proximal Policy Optimization (PPO), have been extensively studied, offline methods, which inherently avoid real-time interactions, may offer practical safety benefits in scenarios like power grid management, where suboptimal policies could lead to severe consequences. To investigate this, we conducted experiments in Grid2Op environments with varying grid complexity, including differences in size and topology. Our results suggest that offline algorithms can achieve comparable or superior performance to online methods, particularly as grid complexity increases. Additionally, we observed that the diversity of training data plays a crucial role, with data collected through environment sampling yielding better results than data generated by trained models. These findings underscore the value of further exploring offline approaches in safety-critical applications.
Paper Presenters
avatar for Angel Peredo

Angel Peredo

United States of America
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

On quantum and LCD codes from the consatacyclic and cyclic codes over the ring IFpr [v]= < vm - v >
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Mohammed Sabiri, Bassou Aouijil
Abstract - Let Rm = IFpr [v]= < vm - v >, where p is an odd prime, IFpr is a finite field with pr elements and vm = v. In this study, we investigate quantum codes over IFpr by using constacyclic codes over Rm, which are dual containing. Furthermore, by using cyclic codes over the ring Rm and their decomposition over the finite field IFpr into cyclic codes, a LCD codes are given as images of LCD codes over Rm.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Personalized Chemotherapy Dosing through Offline Reinforcement Learning
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Hector Lugo, Angel Peredo, Christian Narcia-Macias, Jose Espinoza, Daniel Masamba, Adan Gandarilla, Erik Enriquez, DongChul Kim
Abstract - Cancer continues to be a major global health challenge, with high rates of morbidity and mortality. Traditional chemotherapy regimens often overlook individual patient variability, leading to suboptimal outcomes and significant side effects. This paper presents the application of Reinforcement Learning (RL) and Decision Transformers (DT) for developing personalized chemotherapy strategies. By leveraging offline data and simulated environments, our approach dynamically adjusts dosing strategies based on patient responses, optimizing therapeutic efficacy while minimizing toxicity. Experimental results show that DTs outperform both traditional Constant Dose Regimens (CDR) and online training methods like Proximal Policy Optimization (PPO), leading to improved survival times and reduced mortality. Our findings highlight the potential of RL and DTs to revolutionize cancer treatment by offering more effective and personalized therapeutic options.
Paper Presenters
avatar for Hector Lugo

Hector Lugo

United States of America
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

Survey on Non-Invasive Glucose Monitoring and Glycemia Detection using Machine Learning and Signal Analysis Techniques
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Sharmila Rathod, Aryan Panchal, Krish Ramle, Ashlesha Padvi, Jash Panchal
Abstract - Diabetes or Hyperglycemia, a condition where an individual is characterized by significantly elevated blood sugar levels, may pose a significant threat to the effective lifespan as well as may pose a significant risk for various cardiovascular diseases. Reliable and non-invasive monitoring of hyperglycemia and also hypoglycemia is important for timely intervention and prognosis. The paper presents an extensive and structured survey dealing with the non-invasive glucose monitoring and diabetes detection using machine learning and signal analysis techniques. The paper focuses on a comparative analysis approach which showcases the literature in tabular and diagrammatic form. Examination of 10 papers that deal with Photoplethysmography (PPG) and Electrocardiography (ECG) signals to detect glucose variations using machine learning techniques has been carried out. The review highlights the respective proposed system, unique findings, improvements, techniques, methods, future prospects, comparison with previous studies, feature importance and model evaluation as well as stated accuracy. This comprehensive analysis aims to provide insights into the methodologies in non-invasive glycemic conditions thereby contributing to the development of improved disease analysis.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

TYPOLOGY OF PURPOSE OF GENERATIVE ARTIFICIAL INTELLIGENCE USE
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Anastasia Vitvitskaya, Almaz Galimov
Abstract - We are living in the age of digitalization, a time when the latest technologies are changing everything around us. Artificial intelligence and digitalization have affected all aspects of our life and society. It is important to realise that the Covid-19 pandemic accelerated the development of digital technologies. Technologies of augmented and virtual reality (AR/VR) are used in many fields, including education. Online platforms allowed people to work and study remotely from the comfort of their homes, which made the online format more popular. Now, informal online education and the use of generative artificial intelligence is actively developing, but it is crucial to understand the implications that the active use of artificial intelligence in education will have. The purpose of the study is to identify the tasks for which generative artificial intelligence is used. As a method of research, we used the collection and analysis of scientific literature, as well as the method of survey, in which 750 people answered for which purposes they use artificial intelligence. The article considers theoretical and practical aspects of generative artificial intelligence application, defines and classifies the tasks.
Paper Presenters
avatar for Anastasia Vitvitskaya

Anastasia Vitvitskaya

Russian Federation
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room B London, United Kingdom

4:15pm GMT

A Novel IoT based Solution for Cold Chain Monitoring in the Pharmaceutical Supply Chain
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Vishnu Kumar
Abstract - Cold chain logistics is the process of maintaining a controlled temperature throughout the storage and transportation of temperature-sensitive products. Ensuring the integrity of the cold chain is critical for the safety and efficacy of pharmaceutical (pharma) products. In the modern supply chain land-scape, the pharma industry involves many stakeholders, including Small and Medium-sized Enterprises (SMEs), which handle logistics, storage and retail operations. Despite the availability of advanced temperature monitoring technologies, SMEs face significant challenges in adopting these solutions due to economic constraints, limited technological resources, and lack of expertise. To bridge this gap, this work proposes a novel, cost-effective Internet of Things (IoT) based framework for real-time temperature monitoring in the cold chain of pharma products. Using a Raspberry Pi and Sense HAT module, coupled with a smartphone application, this system enables SMEs to implement an affordable and reliable cold chain monitoring solution. The capabilities of the proposed framework are demonstrated through a temperature monitoring case study, simulating the conditions faced in pharma supply chains. This work is expected to provide a practical resource for SMEs and suppliers seeking to im-prove their cold chain management without incurring excessive costs.
Paper Presenters
avatar for Vishnu Kumar

Vishnu Kumar

United States of America
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Apple Tree Leaves Diseases Detection Using Residual Network
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Simona Filipova-Petrakieva, Petar Matov, Milena Lazarova, Ina Taralova, Jean Jacques Loiseau
Abstract - Plant disease detection plays a key role in modern agriculture, with significant implications for yield management and crop quality. This paper is a continuation of previous research by the authors' team related to the detection of pathologies on apple tree leaves. In order to eliminate the problem of overfitting in the traditional convolutional neural networks (CNNs) transfer learning layers are added to a residual neural network architecture ResNet50. The suggested model is based on pre-trained CNN whose weight coefficients are adapted until ResNet obtains the final classification. The model implementation uses Tensor- Flow and Keras frameworks and is developed in Jupyter Notebook environment. In addition, ImageDataGenerator is utilized for data augmentation and preprocessing to increase the classification accuracy of the proposed model. The model is trained using a dataset of 1821 high-resolution apple leaves images divided into four distinct classes: healthy, multiple diseases, rust, and scab. The experimental results demonstrate the effectiveness of the suggested ResNet architecture that outperforms other state-of-the art deep learning architectures in eliminating the overfitting problem. Identifying different apple leaves pathologies with the proposed model contributes to developing smart agricultural practices.
Paper Presenters
avatar for Petar Matov

Petar Matov

Bulgaria
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Evidence Analysis through Artificial Intelligence Techniques to Facilitate Digital Forensic Investigation and Preparation of Computer Expertise
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Malinka Ivanova, Svetlin Stefanov
Abstract - The growing number and increasing complexity of cyberattacks require investigative experts to use contemporary technologies for finding and analyzing digital evidence and for preparing computer expertise. Artificial intelligence (AI) and machine learning (ML) are among the possibilities for automating a number of routine activities in digital forensics, which can be performed significantly faster and more efficiently. The aim of the paper is to present the potential of AI and ML at analyzing digital evidence as in this case the extraction of text and image information from pdf files is specifically examined. A classification of different types of files that could potentially be located on the victim’s or attacker’s smartphone or computer is also performed using ML algorithm Decision Tree. Synthetically generated files and original scientific papers are utilized for the experiments. The findings point out that the obtained accuracy at classification of file formats, at analyzing and summarizing the content of pdf files is high, which is done thought applying Natural Language Processing techniques and Large Language Models.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Impact of Social Media Algorithms on Community and Cultural Identity: A Sociological Perspective
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Enaam Youssef, Mahra Al Malek, Nagwa Babiker Yousif, Soumaya Abdellatif
Abstract - Social media algorithms are important in suggesting content aligned with users' needs. The relevant technology suggests content and ensures its suitability and relevance to users. Consequently, it is considered an important aspect of everyday life in enhancing community and cultural identity among youth. This research examines the effect of social media algorithms on the community and cultural identity of the young generation in the United Arab Emirates. Theoretically supported by Social Identity Theory, this research gathered data from 341 respondents using structured questionnaires. Results indicated that Social Media Algorithms positively affect Community Identity, implying that these platforms promote a sense of belonging by connecting them to local groups, discussions, and events, strengthening their cultural and social community ties. Results also revealed that the effects of social media algorithms on cultural identity remain positively significant. These findings indicate that social media content improves connection to cultural heritage and shapes cultural identity perceptions, although algorithms sometimes prioritize global over local practices. Overall, these results indicate a robust influence of social media in the UAE as a factor enabling the young generation to seek community identity and cultural belonging, which further helps them retain their overall social identity in the best possible manner. Study findings and limitations are discussed accordingly.
Paper Presenters
avatar for Enaam Youssef

Enaam Youssef

United Arab Emirates
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

Real-Time Cardiovascular Health Monitoring through a Multimodal Data Integration Framework
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Hayat Bihri, Soukaina Sraidi, Haggouni Jamal, Salma Azzouzi, My El Hassan Charaf
Abstract - Predictive analytics and artificial intelligence (AI) offer significant potential to improve healthcare, yet challenges in achieving interoperability across diverse settings, such as long-term care and public health, remain. Enhancing Electronic Health Records (EHRs) with multimodal data provides a more comprehensive view of patient health, leading to better decision-making and patient outcomes. This study proposes a novel framework for real-time cardiovascular disease (CVD) risk prediction and monitoring by integrating medical imaging, clinical variables, and patient narratives from social media. Unlike traditional models that rely solely on structured clinical data, this approach incorporates unstructured insights, improving prediction accuracy and enabling continuous monitoring. The methodology includes modality specific preprocessing: sentiment analysis and Named Entity Recognition (NER) for patient narratives, Convolutional Neural Networks (CNNs)for imaging, and Min-Max scaling with k-Nearest Neighbors (k-NN) imputation for clinical variables. A unique patient identifier ensures precise data fusion through multimodal transformers, with attention mechanisms prioritizing key features. Real-time monitoring leverages streaming natural language processing (NLP) to detect health trends from social media, triggering alerts for healthcare providers. The model undergoes rigorous validation using metrics like AUC-ROC, AUC-PR, Brier score, SHAP values, expert re-views, and clinical indicators, ensuring robustness and relevance.
Paper Presenters
avatar for Hayat Bihri
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

The Proposed of Deep Learning in Recommend Consumer Loan Products to Credit Customers
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Quoc Hung NGUYEN, Xuan Dao NGUYEN THI, Thanh Trung LE, Lam NGUYEN THI
Abstract - With the rapid development of financial technology, financial product recommendation systems play an increasingly important role in enhancing user experience and reducing information search costs, becoming a key factor in the financial services industry. Amid growing competitive pressure, the diversification of user needs, and the continuous expansion of financial products, traditional recommendation systems reveal limitations, especially in terms of accuracy and personalization. Therefore, this study focuses on applying deep learning technology to develop a smarter and more efficient financial product recommendation system. We evaluate this model based on key metrics such as precision, recall, and F1-score to ensure a comprehensive assessment of the proposed approach's effectiveness. Methodologically, we employ the Long Short-Term Memory (LSTM) model, a type of Recurrent Neural Network (RNN) designed to address the challenge of long-term memory retention in time-series data. For the task of recommending the next loan product for customers, LSTM demonstrates its ability to remember crucial information from the distant past, thanks to its gate structure, including input, forget, and output gates. Additionally, the model leverages a robust self-attention mechanism to analyze complex relationships between user behavior and financial product information.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room C London, United Kingdom

4:15pm GMT

A Structured Approach to Software Defect Classification and Explanation: Random Forest and Gradient Boosting Ensembles with a Focus on Prediction Interpretability
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Erick Verdugo, Andy Abad, Remigio Hurtado
Abstract - Software defect prediction is crucial for reducing costs and improving quality. According to a Cutter Consortium report, software defects cause an estimated annual loss of $1.56 trillion in global productivity. Additionally, Tricentis reported that over 30% of software development projects failed due to undetected defects. Undetected defects can increase maintenance costs, delay deliveries, and compromise security, particularly in critical applications such as financial or medical systems. A significant challenge is dealing with imbalanced data, where there are more defect-free modules than defective ones, making detection difficult. This study proposes a four-phase approach: loading and transforming data, using balancing techniques, applying machine learning models, and explaining predictions. Techniques such as SMOTE, ADASYN, and RandomUnderSampling were used to balance the data, applied to models like Random Forest, Gradient Boosting, and SVM. The JM1 dataset, containing software quality metrics and 80% defect-free modules, was used for analysis. Data preprocessing involved imputation, encoding, and normalization. Results show that Random Forest and Gradient Boosting, combined with balancing techniques, achieved the best performance in defect identification. In the future, advanced algorithms such as XGBoost and LightGBM will be explored, and parameter optimization will be conducted to further enhance results. This approach aims to improve defect detection in software and to be applied in other fields.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Automated MLOps-Driven YOLO Framework for Drone-Based Plant Disease Detection
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Salma Mosaad Mohamed Elfeky, Mennaallah Nafady Ahmed Yehia, Ali Hamdi
Abstract - This paper introduces a novel drone-based plant disease detection system optimized for efficient and scalable deployment using MLOps. Utilizing the CADI AI dataset for cashew crop disease classification, it includes automated workflows for iterative training, testing, and deployment across YOLO architectures (YOLOv5, YOLOv8, YOLOv9, and YOLOv10). Advanced data augmentation and incremental dataset expansion, growing from 757 training images to the full dataset, ensure fair evaluations and model optimization. YOLOv5 achieved a peak mAP@50 of 59.4%, followed by YOLOv8 with 50.1%. Iterative finetuning revealed YOLOv9’s superior insect detection performance (mAP@50: 70.9 %) and YOLOv10’s excellence in abiotic stress detection (mAP@50: 77.3%). This study highlights MLOps’ role in real-time model deployment and benchmarking, showcasing robust object detection capabilities and emphasizing iterative optimization and auto-deployment strategies to address dataset imbalance and enhance precision agriculture.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Machine Learning-Driven Node Compaction for Enhanced Hardware Trojan Detection And Run Time Monitoring
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Ananya Deshpande, Akshay Angadi, Amulya H S, Adhokshaja R B, Nirmala Devi M
Abstract - The increasing complexity of ICs and the reliance on external suppliers increase the risk of hardware Trojans, posing significant security threats. Traditional detection methods often fail due to limitations in addressing all potential vulnerabilities. This paper proposes a node compaction technique combined with an XGBoost classifier using features like Vulnerability Factor, Transition Probability, and SCOAP metrics to classify circuit nodes as Trojan-infected or Trojan-free. The compaction reduces execution time and improves real-time monitoring. The checker logic further validates the detection of Trojans by comparing the expected and observed functionality. Validation in TrustHub benchmark circuits demonstrates significant improvements in detection accuracy.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

Performance of the Fast Fourier Transform with NEON Instructions on an ARM Cortex-A72
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Kevin Lajpop
Abstract - The Fast Fourier Transform (FFT) is a fundamental algorithm used in a wide range of applications, from signal processing to cryptography. With the increasing use of embedded and mobile devices, the need to optimize FFT performance has become crucial. This study focuses on the implementation of FFT on an ARM Cortex-A72 processor, leveraging NEON instructions, which are part of the SIMD (Single Instruction, Multiple Data) set. NEON instructions enable parallel operations, resulting in a significant improvement in execution times. Through a comparative analysis between implementations with and without NEON, a 99.99% reduction in execution time was demonstrated when using NEON, highlighting its effectiveness in applications that require high-speed processing, such as post-quantum cryptography.
Paper Presenters
avatar for Kevin Lajpop

Kevin Lajpop

Guatemala
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

PET Image Classification for Lung Cancer Diagnosis: Deep Learning with Transfer Learning, Data Augmentation and Region-Based Prediction Explanation by Integrated Gradients
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Diego Loja, David Alvarado, Remigio Hurtado
Abstract - Lung cancer, one of the leading causes of death worldwide, accounts for more than 2.2 million cases and nearly 1.8 million deaths. This type of cancer is classified into non-small cell lung carcinoma (NSCLC), the most common and slow-progressing type, and small cell lung carcinoma (SCLC), which is less common but highly aggressive [1]. In response to the urgency for rapid and accurate diagnosis, this work presents an innovative method for classifying PET images using the EfficientV2S model, combined with advanced data augmentation and normalization techniques. Unlike traditional methods, this approach incorporates visual explanations based on integrated gradients, enabling the justification of model predictions. The proposed method consists of three phases: data preprocessing, experimentation, and prediction explanation. The LUNGPETCT- DX dataset is utilized, comprising 133 patients distributed across three main classes: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The models are evaluated using quality metrics such as accuracy (78%), precision (82%), recall (78%), and F1-score (76%), highlighting the superior performance of EfficientV2S compared to other approaches. Additionally, integrated gradients are employed to visually justify predictions, providing critical interpretability in the medical context. For future work, the integration of CT images is proposed to enhance predictions, along with validation on larger datasets and optimization through fine-tuning, aiming to improve the model’s generalization and robustness
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom

4:15pm GMT

The Role of Media in Shaping Career Choices: A Content Analysis of Social Media's Influence on College Students' Preference for Influencing Over Traditional Employment in Kerala
Friday February 21, 2025 4:15pm - 5:45pm GMT
Authors - Ebin V Francis, T. Nirmala, Jiby Jose E.
Abstract - This study investigates how media affected the career aspirations of College students in Kerala and finds a tendency associated with social media adjusting over conventional work. Using content analysis of digital media platforms, the research investigates how media content, trends, and narratives influence students’ perceptions of social media as a viable and desirable career path. This study seeks to determine what has changed over the last two decades, including whether peer effects, economic opportunities, or social acceptance are behind this shift. This study offers insights into how the career interests and preferences of the young generation in Kerala influenced by the media landscape can potentially impact the labour market and employment patterns among the youth in the region and aids in understanding the implications of media-influenced occupational aspirations and media patterns among the students in Kerala.
Paper Presenters
Friday February 21, 2025 4:15pm - 5:45pm GMT
Virtual Room D London, United Kingdom
 

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