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 - 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 - Jiahui Yu, Simon Fong, Jinan Fiaidhi, Sabah Mohammed, Richard Millham, Alexandre Lobo, Seon-Phil Jeong, Liansheng Liu Abstract - . Clinical pathways play a vital role in managing diabetes treatment, incorporating medical strategies customized to individual patient needs. In the case of insulin-dependent diabetes mellitus (IDDM), accurately determining insulin dosage and administration timing is essential for regulating blood glucose levels effectively. While general health guidelines exist, they lack personalization. The interactions between medication, lifestyle, and patient conditions are complex, with therapy patterns differing among patients. This paper introduces a combined data stream mining method with fuzzy rule generation to create realtime decision guidelines as clinical pathways for managing IDDM. These guidelines are derived from daily medication records and personal blood glucose trends, generated from continuously updated health data instead of relying on past records. Fuzzy rules are preferred for their ability to personalize treatment, handle complex interactions, adapt in real-time, and provide accurate, timely decisions. They allow for personalization by adapting to individual patient conditions, providing tailored insulin dosages and timings based on real-time data. This method effectively manages the complex interactions between medication, lifestyle, and patient conditions, which can vary greatly among patients. Additionally, fuzzy rules are generated from continuously updated health patterns, ensuring that decision-making reflects the current state of the patient's health rather than relying on outdated historical data. This flexibility accommodates fluctuations in glucose levels due to various factors, making the approach more responsive to both short-term and long-term medical effects. Ultimately, using fresh data leads to more accurate and timely decisions, crucial for maintaining appropriate blood glucose levels in IDDM patients. A computer-based simulation is provided to assess the most appropriate data stream algorithms for this task.
Authors - Assia Boukhamla, Tamer Abderrahmane Lafia, Nabiha Azizi, Samir Brahim Belhaouari Abstract - The high prevalence of cardiovascular diseases (CVDs) worldwide requires accurate diagnostic imaging, particularly through magnetic resonance imaging (MRI). The framework includes preprocessing for region-of-interest segmentation via ViTs, followed by PTQ to reduce model size while maintaining segmentation accuracy Using a small calibration dataset, we apply PTQ to compress the ViT, significantly reducing storage requirements and latency without compromising precision. Experimental results indicate that Float16 quantization achieves an optimal balance between compression rate and segmentation accuracy, demonstrating the feasibility of ViTs for real-time applications.
Authors - Cristinel Gabriel RUSU, Simona MOLDOVANU, Nilanjan DEY, Luminita MORARU Abstract - We are interested in exploring different visual patterns by training ma-chine learning (ML) classifiers on raw and foreground images for MI detection, which has been less studied. In this work, we train machine learning classifiers on raw images (containing background lines) and clear images (containing just foreground/object with background lines removed). Two ECG record datasets containing normal (N) and myocardial infarction (MI) data are analysed via high-level features provided by standard 12-lead ECG signals. Only the limb lead I was cropped from the 12-lead signals to generate the input data. Data augmenta-tion was used for a balanced dataset to prevent overfitting while maintaining the required spatiotemporal invariances for a correct diagnosis. The newly generated ‘clear’ dataset results show that the proposed model achieves high classification performance for the AD, KNN, and RF models, with accuracies that are 32.1%, 27.3%, and 18.5% higher than those of their ‘raw’ counterparts, respectively. These results prove the robustness of the model.
Authors - Volodymyr Kulivnuk, Oleksandr Hladkyi, Alexander Gertsiy, Tetiana Tkachenko, Tetiana Shparaga, Tetiana Mykhailenko, Ihor Vynnychenko, Kateryna Postovitenko, Rostislav Semeniuk Abstract - The development of natural and artificial information systems (IS) in medicine and health tourism is explored. The essence of information resources (IR) and information processes (IP) and their role in medical treatment and health tourism services is investigated. The structural model of the body information resources is substantiated. The information processes occurring in the human body are described. The informaciology model of the information accumulation in the human memory is proposed. The structural model of building principles of human body functional systems (FS) as well as the informaciology model of the human body FS are systemized. The informaciology model of formation of adap-tive results of the human activity is proposed. The natural and artificial infor-mation systems usage in medicine and health tourism is substantiated. The struc-tural models of the natural and artificial information systems are observed. The informaciology resources of artificial IS are explored. The structure of informaci-ology technologies in artificial IS is defined. The structural models of cybernetic systems and artificial (preformed) therapeutic ones are determined.
Authors - Niyantra Mohan Babu, K.Vijayan, Alekhya Devi Malepati Abstract - Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavioral intelligence. Many live out their lives in ignorance due to misdiagnosis or a lack of awareness. Individuals with high-functioning autism or Asperger’s Syndrome are hard to detect using a single method of detection because not every child will show the same symptoms. For example, a child with Asperger’s Syndrome with good eye contact but without social communication skills would be hard to detect using only their gaze points. This paper explores a method for the diagnosis of ASD through the integration of three models: gaze point tracking, quantitative behavioral checklist, and image processing. The eye-tracking technology pinpoints the coordinates of the gaze points and connects them to the training to detect whether the subject has ASD. Studies on this subject have shown discrepancies between detected and actual individuals with autism, as not all autistic children have an irregular gaze. (Yaneva et al. 2020) Insights into the child’s behavioral patterns are offered through Q-CHAT, which quantitatively categorizes the actions of the child. However, the Q-CHAT checklist is one-dimensional and differs in result as the child grows. (Howard et al. 2022) A pre-trained CNN VGG 16 model identifies traits in the children’s facial features as for the image processing model. But, not all individuals with autism have a facial structure that belies their disorder. (Anitha et al. 2024) This paper addresses all the problems through the novel integrated approach of ASD detection using multiple methods.
Authors - Vasileios E. Papageorgiou, Dimitrios-Panagiotis Papageorgiou, Georgios Petmezas, Pan-telis Dogoulis, Nicos Maglaveras, George Tsaklidis Abstract - This study presents a computationally efficient Convolutional Neural Network (CNN) enhanced with transfer learning for medical image classifica-tion. The method was rigorously tested on 3 tumor datasets: brain MRI, and lung and kidney CT scans. It leverages a pre-trained CNN on brain MRI images, fine-tuned with minimal re-training for the CT scans, achieving high classification accuracy. Transfer learning allows the model to adapt to cancer-specific features by utilizing insights from large datasets. Re-training on each tumor type using only 20 epochs, can deliver significant classification performance, demonstrating the method's efficiency. The CNN's computational efficiency ensures it is both accurate and scalable, making it suitable for use in resource-constrained environ-ments. This research highlights the potential of low-complexity deep learning (DL) to accelerate cancer diagnosis while balancing accuracy and efficiency. It shows that complex deep learning models are not always necessary, and optimal performance can be achieved with lower computational costs.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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 - 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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 - 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
