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arXiv 2025年

作者： Syu, Jia-Hao Chun-Wei Lin, Jerry Yu, Philip S. Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Computer Science University of Illinois Chicago United States

As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles (AGVs) in smart factories, which face complex environments and generate large amounts of data. There is an inevitable tradeoff between feature diversity and interference. In this paper, we propose Distributed Multi-Head learning (DMH) systems for power consumption prediction in smart factories. Multi-head learning mechanisms are proposed in DMH to reduce noise interference and improve accuracy. Additionally, DMH systems are designed as distributed and split learning, reducing the client-to-server transmission cost, sharing knowledge without sharing local data and models, and enhancing the privacy and security levels. Experimental results show that the proposed DMH systems rank in the top-2 on most datasets and scenarios. DMH-E system reduces the error of the state-of-the-art systems by 14.5% to 24.0%. Effectiveness studies demonstrate the effectiveness of Pearson correlation-based feature engineering, and feature grouping with the proposed multi-head learning further enhances prediction performance. © 2025, CC BY-NC-SA.

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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Human Behaviour Based Authentication 5th

Human Behaviour Based Authentication

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Harshini, Karella Vaishnavi, Kudurupaka Chandana, Velicharla Rakshitha, M.V. Sreeveda, K. Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Hyderabad Shaikpet India G. Narayanamma Institute of Technology and Science Hyderabad Shaikpet India

ISBN: (纸本)9789819780426

Android is one of the most popular operating systems being used in smart phones and is facing security issues. Many authentication techniques are being used, and most of them are based on username, password or PIN. These techniques are considered weak because of several drawbacks such as passwords and PIN, can be guessed, forgotten or stolen. When the mobile phone is stolen, misplaced or in the possession of the third party, an unauthorized user can get access to the applications and features of the mobile phone. Furthermore, after a one-time login, no further credentials are required. Therefore, a better security authentication system is needed to overcome this problem. This paper proposes an identity management framework that can secure mobile application’s data from an unauthorized user. The proposed framework consists of various features that are extracted from phone typing behaviour. The framework is comprised of enrolment and authentication phase. In the enrolment phase, the system calculates the user typing speed, and in the authentication phase, it can accept or reject the current user based on his typing behaviour. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Mitigation of Sub-Synchronous Oscillations to Improve Grid Stability and Efficiency Using EMT-Based Analysis and Frequency Scanning Algorithms

Mitigation of Sub-Synchronous Oscillations to Improve Grid S...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Muhammad Umar Afzaal Yuqing Dong Yilu Liu Electrical Engineering and Computer Science University of Tennessee Knoxville USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Integrating inverter-based resources (IBRs) into the grid significantly alters the dynamics of modern power systems, and IBRs have been linked to sub-synchronous oscillation (SSO) events worldwide. Identifying the conditions favorable for the occurrence of SSO is essential to ensure the stability of the system and prevent adverse interactions that could damage the components of the power system or even lead to system failure. This paper investigates a specific 20 Hz oscillation event that took place at 5:30 AM on November 21, 2021, at the Kaua'i power system following the tripping of an oil power plant. Using an electromagnetic transient (EMT) model in PSCAD, we performed a sensitivity analysis to examine the conditions and scenarios that contribute to the creation of SSO's. Our findings reveal that specific Synchronous Reference Frame Phase-Locked Loop (SRF-PLL), when interacting with shunt compensation, plays a significant role in the generation of SSO's. To validate these findings, a frequency-scanning-based impedance analysis (FSBIA) is conducted. FSBIA provides a comprehensive assessment of the impedance characteristics of the system on a wide range of frequencies, which is used to identify the stability of the system. The study illustrates that precise tuning of SRF-PLL parameters can effectively mitigate SSO risks.

关键词： Sensitivity analysis Prevention and mitigation Power system dynamics Power system stability Stability analysis Power grids Impedance Phase locked loops Oscillators Tuning

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Asynchronous Federated Learning Based Energy Scheduling for Microgrid-Enabled MEC Network

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IEEE Transactions on Consumer Electronics 2025年

作者： Khan, Haneef Consul, Prakhar Jabbari, Abdoh Duraibi, Salahaldeen Zangoti, Hussein Budhiraja, Ishan Jazan University College of Engineering and Computer Science Department of Electrical and Electronics Engineering Jazan45142 Saudi Arabia IILM University School of Computer Science and Engineering Uttar Pradesh Greater Noida India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida201310 India

Mobile Edge Computing (MEC) is well known for its critical role in controlling the growth of Internet of Things (IoT) applications, even though energy demand forecasting is inherently challenging because of multiple task-specific factors. Including a microgrid as a backup energy source offers a chance to improve the effectiveness of energy distribution, but it also raises concerns about using non-renewable energy sources. This study explores the complex task of managing energy scheduling in a microgrid-enabled MEC network. It does this by minimizing residual planned energy using the conditional value at risk (CVaR) measure and illuminate the complexity of this NP-hard problem. To address this complex problem, a multi-agent stochastic game framework is implemented to achieve a Nash equilibrium for collective policies and prove model convergence, revealing the novel Asynchronous Federated Learning framework for Microgrid-Enabled MEC Network (AFFIN). A device selection strategy and a multi-agent Asynchronous Advantage Actor-Critic (A3C) algorithm with shared neural networks are employed to enhance learning efficiency. The proposed scheme enables localized model training and parameter sharing with the MEC server. The results demonstrated the exceptional effectiveness of incorporating CVaR into energy scheduling, surpassing the performance of random agent and single-agent models in terms of optimizing energy distribution in the MEC network. © 1975-2011 IEEE.

关键词： Clean energy

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Impact of Receiver Orientation on the QoS of the VLC- Enabled Flying Base Station Network 17

Impact of Receiver Orientation on the QoS of the VLC- Enable...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Das, Ankana Panda, Kirtan Gopal Naresh Kumar, Murala Laxmi Sen, Debarati Chakraborty, Sandip IIT G. S. Sanyal School of Telecommunications Kharagpur India IIT Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

The 5G and beyond networks aim to outperform their predecessors in terms of data rate, quality of service (QoS), and latency reduction. Visible light communication (VLC) frequencies are widely explored to enable future-generation communications because of their large, license-free bandwidth. VLC-enabled unmanned aerial vehicles can serve as VLC-enabled flying base stations (V-FBSs) and can significantly contribute towards the increase in coverage and improve the network throughput. However, most studies on VLC-enabled unmanned aerial vehicles have neglected the effect of random receiver orientation in their performance analysis of the networks in terms of outage and data rates. In this paper, we analyze the impact of receiver device orientation on the QoS within the V-FBS network. We thoroughly analyze the permissible degrees of freedom for receiver movement, exploring how it influences the angle of incidence and, consequently, the line of sight (LoS) link between the V-FBS and user equipment (UE). © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Rapid Amplification of Single-Stranded DNA by On-Bead Plasmonic PCR in an Automated Microfluidic System 38

Rapid Amplification of Single-Stranded DNA by On-Bead Plasmo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Anni Bu, Yang Liu, Yuze Lee, Yung Ching Yobas, Levent The Hong Kong University of Science and Technology Division of Bioengineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331508890

This paper presents a novel automated microfluidic system for rapid plasmonic on-beads polymerase chain reaction (PCR). The highly integrated system automates essential PCR workflows, including sample loading, thermal cycling, purification washes, and elution utilizing pneumatic valves. Additionally, gold nanorods are introduced into the PCR system to leverage their efficient photothermal conversion properties to accelerate the PCR reaction. The platform completes 20 PCR cycles within 12 minutes to amplify single-stranded DNA (ssDNA), demonstrating potential for various rapid ssDNA PCR applications, such as aptamer selection and generation. © 2025 IEEE.

关键词： Microfluidics

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Advanced Medical Question and Answering Chatbot Powered by Llama-2 For Precis Health Answer 2

Advanced Medical Question and Answering Chatbot Powered by L...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Anup, S.B. Swetha Chitra, K.S. Poornima, B.M. Raviprakash, B.P. Kant Verma, Kamal Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

LLaMA-2 model, which is known for having the best capabilities in natural language processing at the core of our system. With respect to symptom diagnosis, it ranked among the most accurate in being able to give the chances of as many conditions possible as per the information that it receives from the user. Creating custom responses to unique user queries all the time, which may potentially increase user satisfaction and engagement. We have tested and perfected our chatbot to ensure that the capabilities stood out at a level of accuracy as high as 85 percent-showing outstanding care in the matters of healthcare assistance issues under numerous medical topics. Our work adds advanced features with the improvement techniques to the conventional medical Q and A chatbot. The new deep learning chatbot is not only great for symptom diagnosis but also uses its capabilities to provide context-aware responses based on user health history, lifestyles, and preferences with adjustment of its advice for a more personal healthcare experience. © 2025 IEEE.

关键词： Chatbot LLaMA-2 model medical Question and Answers Natural language processing (NLP) symptom identification

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Machine Learning Techniques for Enhancing Security in Wireless Communication Systems

Machine Learning Techniques for Enhancing Security in Wirele...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Surya, S. Trimbak Ingle, Rajshri Saxena, Megha Poddutoori, Jaipal Reddy Budesh Kanwer, Dr. Kapila, Dhiraj Porur Chennai600116 India Dy Patil College of Engineering Akurdi Maharashtra Pune India Srm Institute of Science & Technology Department of Computer Science & Engineering UP Ghaziabad India Solution Architect Affiliation Bbu DowningtownPA19335 United States Poornima Institute of Engineering and Technology Rajasthan Jaipur India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798331508685

Web of Things or IoT frameworks rely upon wireless communication systems or WCS to accumulate and send measurements, their norm of execution is in danger from security assaults. In IoT frameworks, the Steering Conventions that can be routing protocols for Lower-Power (RPL) and Misfortune Systems administration are fundamental for successful measurements move;in any case, attacks can seriously disable usefulness. Utilizing the change of steering insights, traffic fascination through maverick focuses, and information passages to destructive places, these weaknesses compromise the legitimacy of organizations. To work on RPL's security and execution quality, this examination proposes a progressive AI and ML framework. This strategy joins a support learning calculation for adaptable and versatile highways, an irregular timberland framework for exact traffic classification, and modified RPL goals that consider order results while pursuing directing decisions. Displaying shows that this approach keeps up with negligible jitter while accomplishing extensive enhancements in bundle dispersion extents, network speed, and deferral. Also, it further develops execution quality in IoT-based WCS and solidly gets the RPL convention by accomplishing an extraordinary acknowledgment rate, scarcely any misleading up-sides, and speedy responses to somewhere safe occasions. The study's conclusions provide significant additions to the area and give a thorough way to safeguard RPL against common safety risks © 2025 IEEE.

关键词： Reinforcement learning

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