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Articles 91 - 120 of 677
Full-Text Articles in Computer Sciences
Predicting Skin Concern Severity From Genetic And Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework, Yassine Benachour, Lina Maloukh, Sadok Bouamama, Barbara Geusens
Predicting Skin Concern Severity From Genetic And Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework, Yassine Benachour, Lina Maloukh, Sadok Bouamama, Barbara Geusens
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Personalized dermatology increasingly leverages both genetic predispositions and lifestyle behaviors to model individual skin health outcomes. This study proposes a multi-output machine learning framework to predict the severity of six dermatological phenotypes—acne, redness, dryness, sensitivity, scarring, and pigmentation—using a multimodal dataset of 5,254 individuals. Input features include mutation profiles for six skin-related genes (FLG, MMP1, MMP3, AQP3, SOD2, GPX) and 22 lifestyle variables such as sun exposure, stress, and hydration. We train and evaluate LightGBM models under independent, multi-output, and chained configurations. Performance is assessed using Mean Absolute Error (MAE) and average Quadratic Weighted Kappa (QWK). The proposed ordinal-aware independent …
Enhancing Breast Cancer Detection In Mammographic Imaging Using Explainable Clinical Decision Support System And Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi
Enhancing Breast Cancer Detection In Mammographic Imaging Using Explainable Clinical Decision Support System And Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi
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Breast cancer remains one of the leading causes of mortality among women worldwide, where early and precise detection plays a vital role in improving survival rates and treatment outcomes. However, conventional deep learning approaches often encounter challenges in handling dense mammographic tissues and lack transparency in decision-making, limiting their clinical reliability. To address these limitations, this study introduces TransYOLO-GJO, an explainable and optimized detection framework that integrates transformer-based attention mechanisms into the YOLOv9 architecture and leverages the Golden Jackal Optimization (GJO) algorithm for hyperparameter tuning. The transformer encoder enhances contextual feature extraction, particularly in dense breast regions, while GJO dynamically …
Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua
Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua
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Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) applications. Software-Defined Networking (SDN) is an emerging networking paradigm that extends the lifetime of WSNs by transferring the resource-intensive routing task from sensor nodes to a centralized controller. However, many SDN-based routing schemes for WSNs employ inefficient algorithms at the controller. Traditional shortest-path methods often create traffic imbalances across neighboring nodes, while Reinforcement Learning (RL)-based approaches typically generate excessive control traffic. Both issues accelerate energy depletion and reduce network lifetime. Moreover, existing algorithms frequently overlook critical factors, such as buffer occupancy, when selecting relay nodes, which can lead …
Understanding Medical Information And Emotional Support Needs In Mental Health Questions With Large Language Models, Chen Liu, William Yu Chung Wang, Gohar Khan
Understanding Medical Information And Emotional Support Needs In Mental Health Questions With Large Language Models, Chen Liu, William Yu Chung Wang, Gohar Khan
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Purpose – This study seeks to bridge the gap between users’ multidimensional needs and the single-task capabilities of existing Mental Health Question Answering (MHQA) systems by tackling the underexplored challenge of jointly understanding medical informational needs and emotional support needs within complex consumer mental health inquiries. Design/methodology/approach – Grounded in Rhetorical Structure Theory (RST), the proposed Multi-Needs and Context Recognition (MNCR) framework decomposes mental health question understanding task into four interrelated subtasks: Medical Needs Recognition (MNR), Medical Needs-related Context Extraction (MNCE), Emotional Needs Recognition (ENR) and Emotional Needs-related Context Extraction (ENCE). A new benchmark dataset, MHQ-MedEmo, was constructed through multi-layered …
Dual-Model Approach For Accurate Chest Disease Detection Using Gvit And Swin Transformer V2, Kamal Ahmad, Hafeez Ur Rehman, Babar Shah, Farman Ali, Irfan Hussain
Dual-Model Approach For Accurate Chest Disease Detection Using Gvit And Swin Transformer V2, Kamal Ahmad, Hafeez Ur Rehman, Babar Shah, Farman Ali, Irfan Hussain
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The precise detection and localization of abnormalities in radiological images are very crucial for clinical diagnosis and treatment planning. To build reliable models, large and annotated datasets are required that contain disease labels and abnormality locations. Most of the time, radiologists face challenges in identifying and segmenting thoracic diseases such as COVID-19, Pneumonia, Tuberculosis, and lung cancer due to overlapping visual patterns in X-ray images. This study proposes a dual-model approach: Gated Vision Transformers (GViT) for classification and Swin Transformer V2 for segmentation and localization. GViT successfully identifies thoracic diseases that exhibit similar radiographic features, while Swin Transformer V2 maps …
Privacy, Identity, And Fairness: Unpacking Ethical Influences On Metaverse Adoption In University Learning, Mousa Al-Kfairy, Meera Alalawi, Saed Alrabaee, Omar Alfandi
Privacy, Identity, And Fairness: Unpacking Ethical Influences On Metaverse Adoption In University Learning, Mousa Al-Kfairy, Meera Alalawi, Saed Alrabaee, Omar Alfandi
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As immersive technologies like the Metaverse continue to reshape higher education, it becomes increasingly vital to examine the ethical dimensions shaping student engagement with these platforms. This study investigates how university students perceive privacy, digital identity, informed consent, and algorithmic fairness in Metaverse-based classrooms, and how these perceptions influence their trust and behavioral intention to adopt the technology. A quantitative survey was conducted with 310 university students, all of whom had prior exposure to virtual learning platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0, the study found that Metaverse Ethical Dimensions (MED) significantly influence both Trusting …
Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan
Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan
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Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …
Connecting The Dots: Iot, Sustainability, And Sdgs, Saadat M. Alhashmi, Islam Al-Qudah, Ibrahim Abaker Hashem, Belal Alsinglawi, Raiza Borreo, Hassan S․ Migdadi, Weisi Chen
Connecting The Dots: Iot, Sustainability, And Sdgs, Saadat M. Alhashmi, Islam Al-Qudah, Ibrahim Abaker Hashem, Belal Alsinglawi, Raiza Borreo, Hassan S․ Migdadi, Weisi Chen
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Internet of Things (IoT) technologies can transform various sectors by converging with global sustainability goals. This paper systematically reviews how IoT supports fulfilling the United Nations Sustainable Development Goals (SDGs). This study initially identified publications that are most relevant to IoT and sustainability. Each publication was carefully examined and mapped to its corresponding SDG, methodology, context, and country. This work presents an opportunity to learn about country contributions, collaborations, and IoT and SDG research trends over the past decade. India, China, and the US were among the top contributors to the IoT and SDG literature, with India accounting for 68 …
Embedding-Driven Dual-Branch Approach For Accurate Breast Tumor Cellularity Classification, Hossam Magdy Balaha, Ali Mahmoud, Khadiga M. Ali, Mohammed Ghazal, Norah Saleh Alghamdi, Ashraf Khalil, Ayman El-Baz
Embedding-Driven Dual-Branch Approach For Accurate Breast Tumor Cellularity Classification, Hossam Magdy Balaha, Ali Mahmoud, Khadiga M. Ali, Mohammed Ghazal, Norah Saleh Alghamdi, Ashraf Khalil, Ayman El-Baz
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This study proposes a dual-branch framework for precise classification of breast tumor cellularity via histopathological images where it integrates two distinct branches: the Embedding Extraction Branch (embedding-driven) and the Vision Classification Branch (vision-based). The Embedding Extraction Branch uses the Virchow2 transformation to generate dense, structured embeddings, whereas the Vision Classification Branch employs Nomic AI Embedded Vision v1.5 to process image patches and produce classification logits. Both branches’ outputs are combined to form the final classification. The framework also suggests Knowledge Block with fully connected layers, batch normalization, and dropout to improve feature extraction and reduce overfitting. The proposed approach reports …
Big Data Transfer Service Architecture For Cloud Data Centers: Problems, Methods, Applications, And Future Trends, Muhammad Umar Majigi, Ismaila Idris, Shafi’I Muhammad Abdulhamid, Richard A. Ikuesan
Big Data Transfer Service Architecture For Cloud Data Centers: Problems, Methods, Applications, And Future Trends, Muhammad Umar Majigi, Ismaila Idris, Shafi’I Muhammad Abdulhamid, Richard A. Ikuesan
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Data volume, velocity, and structure have significantly evolved over the years. The complex networking architectures of current infrastructures, and the development, and accessibility of cloud services to a diverse user base have introduced numerous challenges which have raised concerns regarding the quality-of-service performance in data processing for both service providers and customers. Key issues identified in the context of big data transfer services for cloud data centers include storage, big data transfer, service transfer architecture, data processing, bandwidth, and security, all of which demand extensive research. After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data …
Enhancing Smart Contract Security Using A Code Representation And Gan Based Methodology, Dileep Kumar Murala, Samia Loucif, K. Vara Prasada Rao, Habib Hamam
Enhancing Smart Contract Security Using A Code Representation And Gan Based Methodology, Dileep Kumar Murala, Samia Loucif, K. Vara Prasada Rao, Habib Hamam
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Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs …
A Hybrid Fog-Edge Computing Architecture For Real-Time Health Monitoring In Iomt Systems With Optimized Latency And Threat Resilience, Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira
A Hybrid Fog-Edge Computing Architecture For Real-Time Health Monitoring In Iomt Systems With Optimized Latency And Threat Resilience, Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira
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The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing …
Llm-Driven Semantic Explanations For Soil Moisture Prediction Models, Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella
Llm-Driven Semantic Explanations For Soil Moisture Prediction Models, Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella
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Efficient soil moisture prediction is crucial for sustainable agricultural practices, especially in the face of climate change and increasing water scarcity. However, the adoption of machine learning (ML) models in this context is frequently limited by their lack of interpretability, particularly among non-expert users such as farmers. This study proposes a novel approach to soil moisture prediction that combines high predictive performance with enhanced explainability. We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. The ontology formalizes …
An Intelligent Healthcare System For Rare Disease Diagnosis Utilizing Electronic Health Records Based On A Knowledge-Guided Multimodal Transformer Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Ankur Pandey
An Intelligent Healthcare System For Rare Disease Diagnosis Utilizing Electronic Health Records Based On A Knowledge-Guided Multimodal Transformer Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Ankur Pandey
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Rare diseases are a common problem with millions of patients globally, but their diagnosis is difficult because of varied clinical presentations, small sample size, and disparate biomedical data sources. Current diagnostic tools are not able to combine multimodal information effectively, which results in a timely or wrong diagnosis. To fill this gap, this paper suggests a smart multimodal healthcare framework integrating electronic health records (EHRs), genomic sequences, and medical imaging to improve the detection of rare diseases. The framework uses Swin Transformer to extract hierarchical visual features in radiographic scans, Med-BERT and Transformer-XL to learn semantic and long-term temporal relations …
Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari
Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari
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The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …
Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani
Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani
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Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …
A Deep Learning Framework For Automated Breast Cancer Diagnosis Using Intelligent Segmentation And Classification, Ahed Abugabah
A Deep Learning Framework For Automated Breast Cancer Diagnosis Using Intelligent Segmentation And Classification, Ahed Abugabah
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Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution …
Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol
Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol
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This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …
Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh
Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh
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Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …
The Ai-Powered Learning Loop In Higher Education, Oualid Abidi, Vladimir Dzenopoljac, Aleksandra Dzenopoljac
The Ai-Powered Learning Loop In Higher Education, Oualid Abidi, Vladimir Dzenopoljac, Aleksandra Dzenopoljac
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Purpose – This study examines how generative AI tools affect business students’ academic performance by investigating whether flexible AI policies promote deeper learning, enhance self-efficacy and facilitate tacit knowledge acquisition in a Middle Eastern context, while ensuring efficiency and academic integrity. Design/methodology/approach – A qualitative, exploratory study observed 20 final-year business students in Kuwait during five in-class activities using generative AI tools. Semi-structured interviews complemented the researcher’s observations. Thematic analysis revealed patterns in benefits, challenges and learning processes, leading to the development of the AI-powered learning loop framework to explain academic performance outcomes. Findings – The study indicates that generative …
Efficient Smooth Tensor Train And Tensor Ring Completion For Image Classification Enhancement, Salman Ahmadi-Asl, Roman V. Garaev, Rustam A. Lukmanov, Naeim Rezaeian, Asad Masood Khattak, Manuel Mazzara
Efficient Smooth Tensor Train And Tensor Ring Completion For Image Classification Enhancement, Salman Ahmadi-Asl, Roman V. Garaev, Rustam A. Lukmanov, Naeim Rezaeian, Asad Masood Khattak, Manuel Mazzara
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This paper deals with studying the data completion problem for enhancing the image classification task under the pixel removal scenario. In some applications, it happens that a part of the pixels of a given image is lost due to several issues, such as corruption by outliers or artifacts and/or incompleteness due to imprecise data acquisition. This issue results in a completely wrong classification outcome using Deep Neural Networks (DNNs). In this paper we investigate the benefit of data completion in enhancing the classification accuracy of the DNN models to build more robust and stable DNN models. To this end, we …
When Cybersecurity Becomes A Reason For Amending Or Terminating An International Commercial Contract: Proposed Solutions, Mohammed El Hadi El Maknouzi, Enas Mohammed Alqodsi, Iyad Mohammad Jadalhaq, Ashraf Khalil
When Cybersecurity Becomes A Reason For Amending Or Terminating An International Commercial Contract: Proposed Solutions, Mohammed El Hadi El Maknouzi, Enas Mohammed Alqodsi, Iyad Mohammad Jadalhaq, Ashraf Khalil
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This study investigates the issue of a country's cybersecurity evolving into a justification for amending the scope of or terminating an international commercial contract. Therefore, the hypothesis is grounded in the neglect of cybersecurity-related issues during the negotiation of certain types of international commercial contracts, as well as in the drafting of their clauses. This underscores the need for an analytical approach to trace the emergence of such risks. The identification of these risks by the public authority responsible for overseeing cybersecurity may result in the suspension of the performance of the international commercial contract. This measure affects the national …
Web3-Based Identity And Kyc Innovations For Next-Generation Fintech, Usama Arshad, Abdallah Tubaishat, Sajid Anwar, Zahid Halim, Abedallah Abualkishik, Abrar Ullah
Web3-Based Identity And Kyc Innovations For Next-Generation Fintech, Usama Arshad, Abdallah Tubaishat, Sajid Anwar, Zahid Halim, Abedallah Abualkishik, Abrar Ullah
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The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), …
Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma
Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma
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Background: Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored. Objective: This study aimed to investigate whether retrieval-augmented generation techniques, when combined with knowledge graphs (KGs), could improve the contextual relevance and accuracy of AI-driven clinical decision support. For this, we developed and validated a graph-based retrieval-augmented generation (GraphRAG)–enabled local LLM as a clinical support tool for GDM management, assessing its performance against open-source LLM …
Lens: Lightweight And Explainable Llm-Based Apt Detection At The Edge For 6g Security, Suhib Bani Melhem, Muhammed Golec, Abdulmalik Alwarafy, Yaser Khamayseh
Lens: Lightweight And Explainable Llm-Based Apt Detection At The Edge For 6g Security, Suhib Bani Melhem, Muhammed Golec, Abdulmalik Alwarafy, Yaser Khamayseh
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Expected to be deployed in the early 2030s, sixth-generation (6G) wireless networks, with their high speed and integration with cutting-edge technology such as intelligent edge computing, expand the attack surface and face serious cyber threat risks such as Advanced Persistent Threats (APTs). This type of cyber attack can imitate benign network traffic and operate for long periods of time without being detected by traditional detection systems. This paper introduces LENS, a lightweight and explainable LLM-based network security framework designed to address this cybersecurity threat for 6G environments. LENS uses a fine-tuned DistilBERT model to convert raw network streams into natural …
Exploring Students' Perceptions Of Genai Tools In Higher Education: A Case Study, Dina Mansour Tbaishat, Maha Waleed Elfadel
Exploring Students' Perceptions Of Genai Tools In Higher Education: A Case Study, Dina Mansour Tbaishat, Maha Waleed Elfadel
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As artificial intelligence transforms the educational landscape, generative artificial intelligence (GenAI) tools have become influential in enhancing learning experiences. Despite their growing presence in higher education, limited research explores how university students, especially learners in non-Western contexts, perceive these tools. This study investigates students' perceptions at a university in the UAE, focusing on five dimensions: perceived benefits, institutional support, technological self-efficacy, ethical considerations and user satisfaction. The findings reveal that students generally expressed strong agreement regarding the benefits of GenAI, demonstrated a clear sense of ethical awareness and felt confident in their technological abilities. While satisfaction levels were generally high, …
Optimizing Fire Detection In Remote Sensing Imagery For Edge Devices: A Quantization-Enhanced Hybrid Deep Learning Model, Syed Muhammad Salman Bukhari, Nadia Dahmani, Sujan Gyawali, Muhammad Hamza Zafar, Filippo Sanfilippo, Kiran Raja
Optimizing Fire Detection In Remote Sensing Imagery For Edge Devices: A Quantization-Enhanced Hybrid Deep Learning Model, Syed Muhammad Salman Bukhari, Nadia Dahmani, Sujan Gyawali, Muhammad Hamza Zafar, Filippo Sanfilippo, Kiran Raja
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Wildfires are increasing in frequency and severity, presenting critical challenges for timely detection and response, particularly in remote or resource-limited environments. This study introduces the Inception-ResNet Transformer with Quantization (IRTQ), a novel hybrid deep learning (DL) framework that integrates multi-scale feature extraction with global attention and advanced quantization. The proposed model is specifically optimized for edge deployment on platforms such as unmanned aerial vehicles (UAVs), offering a unique combination of high accuracy, low latency, and compact memory footprint. The IRTQ model achieves 98.9% accuracy across diverse datasets and shows strong generalization through cross-dataset validation. Quantization significantly reduces the parameter count …
Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review, Maher Alaraj, Mohammed Radi, Elaf Alsisi, Munir Majdalawieh, Mohamed Darwish
Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review, Maher Alaraj, Mohammed Radi, Elaf Alsisi, Munir Majdalawieh, Mohamed Darwish
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The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging …
A Study Of The Privacy Paradox Amongst Young Adults In The United Arab Emirates, Lena Yuryna Connolly, Michael Lang, Justin Giboney
A Study Of The Privacy Paradox Amongst Young Adults In The United Arab Emirates, Lena Yuryna Connolly, Michael Lang, Justin Giboney
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The rapid digitalisation of society has significantly increased the collection and processing of personal data, raising concerns about individuals’ privacy. The privacy paradox, where individuals express privacy concerns yet continue to disclose personal information, has been widely studied in Western and Asian contexts, but remains underexplored in the Arab world. This study investigates privacy attitudes and behaviors in the United Arab Emirates (UAE), a region at the crossroads of traditional Islamic values and Western influences. Using survey data from 216 Emirati university students, we tested a model that incorporates five constructs: peer interaction and influence, desire for privacy, privacy concerns, …
Enhancing Reliable And Energy-Efficient Uav Communications With Ris And Deep Reinforcement Learning, Wasim Ahmad, Umar Islam, Abdulkadhem A. Abdulkadhem, Babar Shah, Fernando Moreira, Ali Abbas
Enhancing Reliable And Energy-Efficient Uav Communications With Ris And Deep Reinforcement Learning, Wasim Ahmad, Umar Islam, Abdulkadhem A. Abdulkadhem, Babar Shah, Fernando Moreira, Ali Abbas
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The rapid growth in wireless communication demands has led to a surge in research on technologies capable of enhancing communication reliability, coverage, and energy efficiency. Among these, uncrewed aerial vehicles (UAV) and reconfigurable intelligent surfaces (RIS) have emerged as promising solutions. Prior research on using deep reinforcement learning (DRL) to integrate RIS with UAV concentrated on enhancing signal quality and coverage, but it ignored the challenges caused by electromagnetic interference (EMI). This article introduces a novel framework addressing the challenges posed by EMI from Gallium nitride (GaN) power amplifiers in RIS-assisted UAV communication systems. By integrating DRL with quadrature phase …