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Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization

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Scientific Reports 2025年第1期15卷 1-15页

作者： Kathole, Atul B. Singh, Viomesh Kumar Goyal, Ankur Kant, Shiv Savyanavar, Amit Sadanand Ubale, Swapnaja Amol Jain, Prince Islam, Mohammad Tariqul Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune 411018 India Artificial Intelligence and Data Science Vishwakarma Institute of Technology (VIT) Pune India Symbiosis Institute of Technology Symbiosis International (Deemed University) Maharashtra Pune 412115 India Department of Computer Science and Engineering (AI & DS) Greater Noida Institute of Technology (GNIOT) Delhi/NCR Greater Noida India Department of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune 411038 India Information Technology Department Marathwada Mitra Mandal College of Engineering Pune India Department of Mechatronics Engineering Parul Institute of Technology Parul University Gujarat Vadodara 391760 India Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment UKM Bangi Selangor 43600 Malaysia

Load balancing (LB) is a critical aspect of Cloud computing (CC), enabling efficient access to virtualized resources over the internet. It ensures optimal resource utilization and smooth system operation by distributing workloads across multiple servers, preventing any server from being overburdened or underutilized. This process enhances system reliability, resource efficiency, and overall performance. As cloud computing expands, effective resource management becomes increasingly important, particularly in distributed environments. This study proposes a novel approach to resource prediction for cloud network load balancing, incorporating federated learning within a blockchain framework for secure and distributed management. The model leverages Dilated and Attention-based 1-Dimensional Convolutional Neural Networks with bidirectional long short-term memory (DA-DBL) to predict resource needs based on factors such as processing time, reaction time, and resource availability. The integration of the Random Opposition Coati Optimization Algorithm (RO-COA) enables flexible and efficient load distribution in response to real-time network changes. The proposed method is evaluated on various metrics, including active servers, makespan, Quality of Service (QoS), resource utilization, and power consumption, outperforming existing approaches. The results demonstrate that the combination of federated learning and the RO-COA-based load balancing method offers a robust solution for enhancing cloud resource management. © The Author(s) 2025.

关键词： Cloud Dilated Federated learning Load balancing Random opposition of Coati Optimization algorithm Resource prediction Resource sharing

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data-driven Control of T-Product-based Dynamical Systems

arXiv

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arXiv 2025年

作者： He, Ziqin Mei, Yidan Mei, Shenghan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), where the system evolution is governed by the T-product between a third-order dynamic tensor and a third-order state tensor. In particular, we offer necessary and sufficient conditions to determine the data informativity for system identification, stabilization by state feedback, and T-product quadratic regulation of TPDSs with detailed complexity analyses. Finally, we validate our framework through numerical *** Codes 37N35, 93-08, 93A15 © 2025, CC BY.

关键词： Tensors

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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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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Constraint-Guided Learning of data-driven Health Indicator Models: An Application on the Pronostia Bearing dataset

arXiv

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arXiv 2025年

作者： Tefera, Yonas Van Baelen, Quinten Meire, Maarten Luca, Stijn Karsmakers, Peter KU Leuven Dept. of Computer Science ADVISE-DTAI Kleinhoefstraat 4 GeelB-2440 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Flanders Make @ KU Leuven Belgium Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Ghent University Department of Data Analysis and Mathematical Modelling Coupure Links 653 Gent9000 Belgium

This paper presents a constraint-guided deep learning framework to develop physically consistent health indicators in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete knowledge of complex systems. To address this, we integrate domain knowledge into deep learning models via constraints, ensuring monotonicity, bounding output ranges between 1 and 0 (representing healthy to failed states respectively), and maintaining consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing to incorporate domain knowledge. We implement a constraint-guided gradient descent optimization within an autoencoder architecture, creating a constrained autoencoder. However, the framework is flexible and can be applied to other architectures as well. Using time-frequency representations of accelerometer signals from the Pronostia dataset, the constrained model generates more accurate and reliable representations of bearing health compared to conventional methods. It produces smoother degradation profiles that align with the expected physical behavior. Model performance is assessed using three metrics: trendability, robustness, and consistency. When compared to a conventional baseline model, the constrained model shows significant improvement in all three metrics. Another baseline incorporated the monotonicity behavior directly into the loss function using a soft-ranking approach. While this approach outperforms the constrained model in trendability, due to its explicit monotonicity enforcement, the constrained model performed better in robustness and consistency, providing stable and interpretable health indicator estimates over time. The results of the ablation study confirm that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health indicator con

关键词： Electronic health record

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A Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints 19thth

A Survey of Machine Learning Methods for Analyzing Synovit...

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19th International Conference on computer Aided Systems Theory, EUROCAST 2024

作者： Bąk, Artur Klempous, Ryszard Segen, Jakub Nikodem, Jan Rozenblit, Jerzy Chaczko, Zenon Kulbacki, Michal Gruszecka, Katarzyna Skoczyńska, Marta Atsushi, Ito Bożejko, Wojciech Jagielski, Dariusz Panejko, Anna Kowalczyk, Hubert Kluwak, Konrad Wojciechowski, Konrad Kulbacki, Marek Wroclaw University of Technology Wroclaw Poland Wroclaw University of Science and Technology Wroclaw Poland School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia Department of Electrical and Computer Engineering University of Arizona Tucson United States Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland Mathematical Institute University of Wroclaw Wroclaw Poland Faculty of Economics Chuo University Tokyo Japan Department of Rheumatology and Internal Diseases Lower Silesian Specialist Hospital Wroclaw Poland

ISBN: (纸本)9783031829598

Synovitis, characterized by inflammation of the synovial membrane in human joints, poses significant diagnostic and treatment challenges. This review presents methods and recent machine learning (ML) developments to analyze synovial arthritis in joints that help overcome these challenges. The methods described in the review include traditional ML algorithms, novel deep learning architectures and recent medical imaging techniques. Key challenges are discussed including the need for large and diverse datasets, model interpretability, generalization to different patient populations, dealing with data variability, and reducing computational complexity. The review also examines integrating multimodal data sources, advances in transfer learning, and developing robust, interpretable models as future directions. It includes enhancing early diagnostic capabilities, leveraging joint-on-a-chip simulations, and investigating signaling pathways in rheumatoid arthritis. This study aims to provide a consolidated resource for interdisciplinary researchers, clinicians and practitioners in the fields of rheumatology and medical imaging as it synthesizes current research to understand better ML methods in the detection of synovitis in human joints, paving the way for improved diagnostic and care capabilities over the patient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices 19thth

Medical Protocols and AI-Driven Algorithms for Enhanced Mo...

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19th International Conference on computer Aided Systems Theory, EUROCAST 2024

作者： Kulbacki, Marek Segen, Jakub Chaczko, Zenon Kulbacki, Michal Nikodem, Jan Klempous, Ryszard Bąk, Artur Rozenblit, Jerzy Zyśko, Dorota Hrymniak, Bruno Jagielski, Dariusz Wroclaw University of Technology Wroclaw Poland Wroclaw University of Science and Technology Wroclaw Poland School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia Department of Electrical and Computer Engineering University of Arizona Tucson United States Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland Mathematical Institute University of Wroclaw Wroclaw Poland Center for Heart Diseases 4th Military Hospital Wroclaw Poland

ISBN: (纸本)9783031829598

Implantable cardioverter-defibrillators (ICDs) present several challenges regarding their detection, localization, and continuous monitoring within patients’ bodies. These challenges include maintaining the integrity of electrodes, preventing local tissue irritation, avoiding device displacement, and ensuring compatibility with MRI. Continuous monitoring is critical to ensure patient safety and the functional integrity of the devices. This paper systematically reviews approaches for the comprehensive monitoring of Cardiac Implantable Electronic Devices (CIEDs) over the past ten years, focusing on the CaRDIA-X procedure. We examine advanced protocols and integrated algorithms designed for this purpose. Specifically, we introduce the CaRDIA-X protocol, a manual method for identifying CIEDs based on the analysis of device patterns and the arrangement of elements on X-ray images. We compare this protocol with alternative detection methods, including artificial intelligence algorithms, and emphasize the field’s most significant studies and comprehensive comparative analyses. In conclusion, we propose improvements to the classic CaRDIA-X algorithm, focusing on automating the CaRDIA-X procedure, which holds significant potential for enhancing patient management and therapeutic efficacy. This integrated proposition offers a holistic perspective on CIED performance and potential issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic medical equipment

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Evaluation of carbon based interconnects for digital signalling in printed flexible electronics on sustainable substrates

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年

作者： Wang, Zixin Saleh, Mahdi Alwattar, Aula Alfredsson, Maria Horne, Robert J. Ang, Chee S. Condell, Joan Flowers, Emily Matcham, Faith Robinson, Tony Rooksby, John Saha, Sian Rose, Louise Batchelor, John C. Casson, Alexander J. Henry Royce Institute for Advanced Materials Manchester United Kingdom The University of Manchester School of Engineering Department of Electrical and Electronic Engineering Manchester United Kingdom The University of Manchester School of Natural Sciences Department of Chemistry Manchester United Kingdom University of Kent School of Chemistry and Forensic Science Canterbury United Kingdom University of Kent School of Engineering Canterbury United Kingdom University of Kent School of Computing Canterbury United Kingdom Ulster University School of Computing Engineering and Intelligent Systems Derry United Kingdom King’s College London Division of Digital Health and Applied Technology Assessment London United Kingdom University of Sussex School of Psychology Falmer United Kingdom Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

Printed electronics using flexible substrates are an emerging area, allowing next-generation electronics to conform and flex with different surfaces, from human skin to clothing. In the hybrid integration, or sea-of-rigids, approach, conventional microchips are mounted onto (generally) plastic substrates such as polyethylene naphthalate (PEN), with (typically) printed silver tracks for interconnections between components. An ongoing research direction is to replace plastic substrates with biodegradable substrates and to replace silver tracks with non-heavy metal-based tracks. While the substrates and tracks form only part of an overall system, replacing them is a step towards increased sustainability and helps to meet net-zero goals for printed electronic systems. Previously, several papers have investigated printed carbon tracks for low-frequency analog sensing applications. This paper explores the feasibility of using printed carbon tracks on biodegradable substrates for high-frequency applications such as digital signaling over a Serial-Peripheral Interface (SPI). We investigate the printability, thermal stability, and electrical conductivity of carbon ink screen-printed onto six commercially available sustainable and flexible substrates. Our results demonstrate that multi-layer screen printing substantially reduced the electrical resistance of carbon tracks, enabling SPI communication at frequencies up to 16 MHz with three layers of carbon ink. A Natureflex™ substrate provided the best balance of printability, thermal stability, and electrical performance. Substrates such as greaseproof paper and ClearFilm PU showed potential for flexible electronics, but require further optimization. This study provides valuable insights into selecting and optimizing biodegradable substrates for high-frequency digital systems, supporting the move towards more sustainable printed electronics. © 2022 IEEE.

关键词： Variable frequency oscillators

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Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty

arXiv

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arXiv 2025年

作者： Jain, Yoshee Hollander, John He, Amber Tang, Sunny Zhang, Liang Sabatini, John Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72401 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Psychology University of Memphis MemphisTN38152 United States

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation;however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI’s GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional Systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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