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arXiv 2024年

作者： Tobin, Joshua Black, Michaela Ng, James Rankin, Debbie Wallace, Jonathan Hughes, Catherine Hoey, Leane Moore, Adrian Wang, Jinling Horigan, Geraldine Carlin, Paul McNulty, Helene Molloy, Anne M. Zhang, Mimi School of Computer Science & Statistics Trinity College Dublin Dublin Ireland School of Computing Engineering & Intelligent Systems Ulster University Derry Londonderry United Kingdom School of Computing Ulster University Belfast United Kingdom School of Biomedical Sciences Nutrition Innovation Centre for Food and Health Ulster University Coleraine United Kingdom School of Geographic & Environmental Sciences Ulster University Coleraine United Kingdom School of Health Wellbeing & Social Care The Open University Belfast United Kingdom School of Medicine Trinity College Dublin Dublin Ireland

The Latent Block Model (LBM) is a prominent model-based co-clustering method, returning parametric representations of each block cluster and allowing the use of well-grounded model selection methods. The LBM, while adapted in literature to handle different feature types, cannot be applied to datasets consisting of multiple disjoint sets of features, termed views, for a common set of observations. In this work, we introduce the multi-view LBM, extending the LBM method to multi-view data, where each view marginally follows an LBM. In the case of two views, the dependence between them is captured by a cluster membership matrix, and we aim to learn the structure of this matrix. We develop a likelihood-based approach in which parameter estimation uses a stochastic EM algorithm integrating a Gibbs sampler, and an ICL criterion is derived to determine the number of row and column clusters in each view. To motivate the application of multi-view methods, we extend recent work developing hypothesis tests for the null hypothesis that clusters of observations in each view are independent of each other. The testing procedure is integrated into the model estimation strategy. Furthermore, we introduce a penalty scheme to generate sparse row clusterings. We verify the performance of the developed algorithm using synthetic datasets, and provide guidance for optimal parameter selection. Finally, the multi-view co-clustering method is applied to a complex genomics dataset, and is shown to provide new insights for high-dimension multi-view problems. © 2024, CC BY.

关键词： Genome

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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The Effect of Prolonged Conditionally Automated Driving on Fatigue, Physiological Activity, and Takeover Performance: A Driving Simulator Experiment

SSRN

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SSRN 2025年

作者： Coyne, Rory Khan, Bilal Alam Shiel, Matthew Ur Rahman, Shams Vlad, Victor Lillis, Conor Usman, Muzna Kielty, Paul Parsi, Ashkan Lemley, Joseph Smeaton, Alan F. Corcoran, Peter Walsh, Jane C. School of Psychology University of Galway Ireland School of Psychology Trinity College Dublin Ireland FotoNation Ltd Block 5 Parkmore East Galway Ireland Insight Research Ireland Centre for Data Analytics Dublin City University Dublin Ireland School of Computer Science University of Galway Ireland Department of Electrical and Electronic Engineering University of Galway Ireland School of Computing Dublin City University Dublin Ireland

The transition from manual to conditionally automated driving involves the driver’s role changing from an active operator to a ‘fallback-ready’ user, who is free to disengage from driving, but must remain ready to take over when prompted. Driver fatigue, which can be distinguished from drowsiness and already accounts for as many as 20% of all road traffic accidents, is likely to remain a pervasive problem during conditionally automated driving, as reductions in attention and vigilance due to fatigue could imperil safe transitions of control between automated system and user. While considerable research exists concerning drivers’ responses under states of drowsiness or distraction, fewer studies have investigated the effect of automation on fatigue. This driving simulator experiment was conducted to examine the effect of prolonged automation on fatigue during conditionally automated driving. Drivers’ self-reported fatigue and workload were measured, in addition to a suite of physiological measures and takeover reaction time and quality. Findings show that fatigue was significantly higher following 50 minutes of automated driving than at baseline, or while participants performed a non-driving-related task. Strikingly, 80% of participants experienced signs of sleepiness, and almost half had to exercise effort to stay awake. Fatigue also resulted in decreases in heart rate and relative beta power derived from electroencephalography, and an increase in blink rate and heart rate variability. Overall, the findings advance knowledge in this area by supporting the idea of fatigue as failure to adequately self-regulate during automation. Several physiological measures have also been identified as possible markers of fatigue to inform emerging monitoring technology. © 2025, The Authors. All rights reserved.

关键词： Heart

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Effect of Cost on Internet of Things-Based Integrated Remote Electronic Health Surveillance, Self-Regulating Home, and Security Alert System: A Review Analysis

Effect of Cost on Internet of Things-Based Integrated Remote...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Anjali Naudiyal Shobha Aswal Harishchander Anandaram Kapil Joshi Kawerinder Singh Sidhu Ajay Singh School of Applied and Life Sciences Uttaranchal University Dehradun India School of Computing Graphic Era Hill University Dehradun Uttarakhand India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Computer Science and Engineering Uttaranchal University Dehradun Uttarakhand India Uttaranchal University Dehradun Uttarakhand India

Auto-control and the internet of things have brought about significant changes around the globe. With each passing day, the expansion and revolution in the field of contemporary technology brought on by the Internet of Things make our life simpler and more convenient by automating formerly manual tasks. In the modern period, a person's level of risk to their health is not dependent on their age because hectic schedules and erratic lifestyles are common. This study focuses on the price of IOT gadgets with sensors in various industries. It has been made clear through this article that it is difficult to integrate smart devices across all industries and nations due to their high cost.

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An Integrated Web Solution for Planetary Data Visualization and Exploration

An Integrated Web Solution for Planetary Data Visualization ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Ananya Maheshwari Love Lakhwani Pariav Naik Abhishek Kumar Jai G. Singla Amitabh Nitant Dube VijayPal Singh Dhaka School of Computing and Intelligent Systems Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Department of Computer Engineering Charotar University of Science and Technology Anand Gujarat India Department of Electrical Engineering Indian Institute of Technology Bhubaneshwar Bhubaneshwar Odisha India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad Gujarat India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The exploration and study of planetary bodies have produced vast amounts of geospatial data, creating a need for effective visualization and analysis tools. Many existing solutions are constrained by platform limitations and are resource-intensive, reducing their accessibility for broader use. To overcome these challenges, we developed a lightweight, web-based platform that visualizes interplanetary data and supports multiple geospatial data formats, including shapefiles, KML, and CZML. Our solution includes unique features such as 2D and 2.5D terrain visualization, 3D tiles, and tools for navigating to specific coordinates and measuring distances. These enhancements allow users to explore and interact with geospatial data from various planetary bodies across multiple devices without requiring specialized hardware or software. By modifying the underlying system, we extended its application to planetary data while preserving accessibility and performance across different platforms. This paper discusses the methodology behind these customizations, the challenges we encountered, and the potential applications of our platform in scientific research and education.

关键词： Performance evaluation Three-dimensional displays Coordinate measuring machines Navigation Data visualization Geoscience and remote sensing Extraterrestrial measurements Software Hardware Geospatial analysis

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Robust Signal Processing Approaches for Enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges

Robust Signal Processing Approaches for Enhancing Communicat...

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Automation and Computation (AUTOCOM), International Conference on

作者： Rashmy Moray Savita Kumari Deepak Upadhyay Rashmi Sharma Aakash Singh Ranswal Nookala Venu Symbiosis Institute of Management Studies (SIMS) A constituent of Symbiosis International Deemed University School of Computing Science and Engineering Galgotias University Greater Noida India Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks.

关键词： Fading channels Wireless communication Spectral efficiency Prevention and mitigation Channel estimation Massive MIMO Energy efficiency Hardware Reliability Contamination

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SmartRecovery: A Deep Learning-Based System for Personalized Post-Cold Recovery Management in Diabetic Patients

SmartRecovery: A Deep Learning-Based System for Personalized...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： G. Revathy J. Justina Princy Thilagavathy C Saranya M. Thangavel Department of CSE Srinivasa Ramunjan Centre Sastra Deemed University Kumbakonam Tamil Nadu Department of Computer App ications School of Computing VET Institute of Arts and Science (Co-education) College Thindal. Erode Tamil Nadu Department of Biomedical Engineering Nandha Engineering College Erode Tamil Nadu Department of MCA Erode Sengunthar Engineering College PerunduraiErode Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

People with diabetes frequently have a longer and more difficult time recovering from colds because of their weakened immune systems and altered metabolism. An method that is more individualized is required since traditional recovery regimens do not take into consideration lifestyle variables, pharmaceutical interactions, and individual differences in glucose levels. A deep learning-based solution called SmartRecovery is suggested in this study to help diabetics control their post-cold recovery as best they can. The system creates adaptive recovery plans by utilizing multimodal data, such as patient vitals, glucose monitoring, medication adherence, and symptom progression. Using a combination of recurrent neural networks (RNNs) for sequential health data analysis and convolutional neural networks (CNNs) for pattern recognition, SmartRecovery predicts potential complications and recommends personalized interventions. In order to ensure ongoing evaluation and feedback, the system incorporates real-time monitoring via wearable sensors and mobile health applications. Experimental assessments using actual diabetes patient datasets show how well the suggested strategy works to speed up recovery and reduce dangers to health. A revolutionary approach to post-illness care, SmartRecovery improves patient outcomes and lessens the strain on healthcare systems by utilizing AI-driven insights.

关键词： Deep learning Telemedicine Real-time systems Diabetes Pattern recognition Glucose Monitoring Wearable sensors Standards Strain

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Energy efficient task offloading from IoHT in delay sensitive edge networks

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Alexandria engineering Journal 2025年 128卷 476-483页

作者： Amit Kumar Jitendra Kumar Samriya Ashok Bhansali Meena Malik Varsha Arya Wadee Alhalabi Eman Alharbi Brij B. Gupta Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Indian Institute of Information Technology Sonepat India Department of Computer Engineering and Applications GLA University Mathura India Department of CSE Chandigarh University Mohali India Hong Kong Metropolitan University (HKMU) Hong Kong Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Immersive Virtual Reality Research Group Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Cybersecurity Korea University Seoul South Korea Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul 02447 South Korea

The edge server connected to the Internet of Things (IoT) is designed to provide optimized resource allocation and task processing, particularly for delay-sensitive applications. This becomes especially challenging when certain tasks demand lower response times compared to others. In this paper, we develop a deep learning model to optimize the task offloading problem in a multi-user Internet of Health Things (IoHT) edge environment. The proposed model, based on a Deep Reinforcement Learning (DRL) architecture, is trained on various input data to manage task offloading across data centres within the edge network. It effectively addresses the challenges of optimizing both offloading probability and power allocation. Simulations are conducted to evaluate the energy efficiency of IoHT devices and their computational offloading capabilities in the cloud. The results demonstrate that the proposed method achieves a higher response rate and greater energy savings compared to existing approaches.

关键词： Delay Edge networks Offloading tasks IOHT

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Eavesdropper-Aware Optimization of Cooperative Relaying Networks in the Presence of Heterogeneous Interference

Eavesdropper-Aware Optimization of Cooperative Relaying Netw...

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Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Deepak Upadhyay Anil Kumar Sonia Rani Deepak Suresh Asudani Aakash Singh Ranswal Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computing DIT University Dehradun India School of Nursing Prof. Mental Health Nursing Galgotias University Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

To improve the secure communication performance in untrusted heterogeneous interference environments, this paper designs an eavesdropper-aware optimization model for CRNs. By using a combination of sophisticated optimization approaches, the model updates resource distributions and relay deployments on the fly boosting secrecy rates and slashing outage probabilities in one fell swoop. Extensive simulations show that the model is effective at reducing these effects under the varying nature of interference attacks (including co-channel and jamming) while keeping the performance of the system. A comparative analysis with existing models shows the improved capability of this method to provide unbeatable security along-with increased throughput and less energy. This research lays a foundation for investigation in secure communications, bringing advantages of enhanced robustness against diversified eavesdropping threats at different tiers within sophisticated networks.

关键词： Wireless communication System performance Interference Probability Throughput Robustness Power system reliability Security Resource management Optimization

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Framework, Standards, Applications and Best practices of Responsible AI: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gadekallu, Thippa Reddy Dev, Kapal Khowaja, Sunder Ali Wang, Weizheng Feng, Hailin Fang, Kai Pandya, Sharnil Wang, Wei College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Department of Computer Science ADAPT Centre Munster Technological University Bishopstown CorkT12 P928 Ireland School of Computing Faculty of Engineering and Computing Dublin City University Glasnevin Campus DublinD09 V209 Ireland Department of Computer Science City University of Hong Kong Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China

Responsible Artificial Intelligence (RAI) is a combination of ethics associated with the usage of artificial intelligence aligned with the common and standard frameworks. This survey paper extensively discusses the global and national standards, applications of RAI, current technology and ongoing projects using RAI, and possible challenges in implementing and designing RAI in the industries and projects based on AI. Currently, ethical standards and implementation of RAI are decoupled which caters each industry to follow their own standards to use AI ethically. Many global firms and government organizations are taking necessary initiatives to design a common and standard framework. Social pressure and unethical way of using AI forces the RAI design rather than implementation. © 2025, CC BY.

关键词： Ethical technology

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