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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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 DLondon, United Kingdom
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
