检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Li, Weicai Lv, Tiejun Ni, Wei Zhao, Jingbo Hossain, Ekram Poor, H. Vincent Beijing100876 China School of Electrical and Data Engineering University of Technology SydneyNSW Australia School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia The School of Computing Science and Engineering University of New South Wales NSW2052 Australia Department of Electrical and Computer Engineering University of Manitoba Canada Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which are inefficient when not all nodes in the network are DFL clients. This paper puts forth a new D-FL strategy, termed Route-and-Aggregate (R&A) D-FL, where participating clients exchange models with their peers through established routes (as opposed to flooding) and adaptively normalize their aggregation coefficients to compensate for communication errors. The impact of routing and imperfect links on the convergence of R&A D-FL is analyzed, revealing that convergence is minimized when routes with the minimum end-to-end packet error rates are employed to deliver models. Our analysis is experimentally validated through three image classification tasks and two next-word prediction tasks, utilizing widely recognized datasets and models. R&A DFL outperforms the flooding-based D-FL method in terms of training accuracy by 35% in our tested 10-client network, and shows strong synergy between D-FL and networking. In another test with 10 D-FL clients, the training accuracy of R&A D-FL with communication errors approaches that of the ideal C-FL without communication errors, as the number of routing nodes (i.e., nodes that do not participate in the training of D-FL) rises to 28. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Healthy/Glaucoma Fundus Retinal Image Classification using Butterfly Algorithm Optimized ResNet-Features

引用

Procedia computer Science 2025年 258卷 1804-1812页

作者： Ramya Mohan Seifedine Kadry Sahar Yassine Venkatesan Rajinikanth Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai 602105 TN India Department of applied data science Noroff University College Kristiansand Norway

Hospitals frequently use medical image supported disease diagnosis to identify diseases and evaluates their severity. Automation in image examination is normally preferred to reduce the diagnostic burden. Developing a Deep-Learning Tool (DLT) to analyze the fundus-photography (FP) labeled as healthy/glaucoma class is the aim of this research. Different phases in this DLT involves the following; collection of FP database from image repository and resizing it to 224x224x3 pixels, feature extraction using ResNet (RN) model, feature reduction using the Brownian-Butterfly Algorithm (BBA), serial concatenation of features to obtain fused-features (FF), and executing the binary classification using 3-fold cross validation. This work utilized a two-fold training in order to improve detection accuracy with the FP data. This investigation is executed using individual and FF to verify the DLT’s performance. The results of this research validates that, the K-Nearest Neighbor (KNN) assisted classification provides 100% accuracy when FF based investigation is executed.

关键词： Healthcare global health fundus image ResNet butterfly algorithm classification

来源：评论

学校读者我要写书评

暂无评论

Software-Defined Number Formats for High-Speed Belief Propagation

引用

IEEE Transactions on Emerging Topics in Computing 2025年

作者： Molahosseini, Amir Sabbagh Lee, Junkyu Vandierendonck, Hans School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Institute for Analytics and Data Science University of Essex United Kingdom

This paper presents the design and implementation of Software-Defined Floating-Point (SDF) number formats for high-speed implementation of the Belief Propagation (BP) algorithm. SDF formats are designed specifically to meet the numeric needs of the computation and are more compact representations of the data. They reduce memory footprint and memory bandwidth requirements without sacrificing accuracy, given that BP for loopy graphs inherently involves algorithmic errors. This paper designs several SDF formats for sum-product BP applications by careful analysis of the computation. Our theoretical analysis leads to the design of 16-bit (half-precision) and 8-bit (mini-precision) widths. We moreover present highly efficient software implementation of the proposed SDF formats which is centered around conversion to hardware-supported single-precision arithmetic hardware. Our solution demonstrates negligible conversion overhead on commercially available CPUs. For Ising grids with sizes from 100×100 to 500×500, the 16- and 8-bit SDF formats along with our conversion module produce equivalent accuracy to double-precision floating-point format but with 2.86× speedups on average on an Intel Xeon processor. Particularly, increasing the grid size results in higher speed-up. For example, the proposed half-precision format with 3-bit exponent and 13-bit mantissa achieved the minimum and maximum speedups of 1.30× and 1.39× over single-precision, and 2.55× and 3.40× over double-precision, by increasing grid size from 100×100 to 500×500. © 2013 IEEE.

关键词： Digital arithmetic

来源：评论

学校读者我要写书评

暂无评论

Functional Testing and Repair for ReRAM-Based Deep-Learning Accelerators

IEEE Design and Test

引用

IEEE Design and Test 2025年第3期42卷 66-73页

作者： Li, Wen Wang, Ying Li, Huawei Li, Xiaowei Zou, Kaiwei Shanxi University School of Computer and Information Technology Shanxi Taiyuan030006 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Editor’s notes: This article offers an overview of the RRAMedy framework, designed to perform online testing, fault diagnosis, and fault mitigation in resistive random-access memory (ReRAM) devices, specifically in the context of ReRAM crossbar-based implementations for AI hardware acceleration. —Jyotika Athavale, Synopsys, USA —Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, France. © 2013 IEEE.

关键词： Accuracy Fault tolerant systems Fault tolerance Deep learning Image edge detection Fault detection Computational modeling Training Reliability Hafnium

来源：评论

学校读者我要写书评

暂无评论

Optimal Green Energy Estimation for Sustainable Electric Vehicle Networks Using Queueing-Based Retrial Process 1

Optimal Green Energy Estimation for Sustainable Electric Veh...

引用

1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Varshney, Shreekant Raval, Jainit Priyadarshi, Rahul Manek, Vidhi Dadheech, Yash Shah, Manthan Pandit Deendayal Energy University School of Technology Department of Mathematics Gujarat Gandhinagar382426 India Pandit Deendayal Energy University School of Energy Technology Department of Electrical Engineering Gujarat Gandhinagar382426 India Symbiosis International University Symbiosis Institute of International Business Hinjewadi Maharashtra Pune411057 India New York University Center for Data Science Department of Center for Data Science New YorkNY United States Pandit Deendayal Energy University School of Technology Department of Computer Science and Engineering Gujarat Gandhinagar382426 India Pandit Deendayal Energy University School of Technology Department of Mechanical Engineering Gujarat Gandhinagar382426 India

ISBN: (纸本)9798331520540

This paper presents a stochastic model for addressing a single-server service station offering both EV charging and battery swapping services, considering the realistic retrial-based queueing behavior of the waiting individuals. A computationally efficient method is proposed for determining the steady-state probability distribution of the charging network. The approach involves analyzing the governing differential-difference equations and employing a matrix method to derive the steady-state probability distribution. Further, straightforward formulae are derived for critical system performance indicators, considering both server and system characteristics. Numerical results are visualized in the form of different graphical representations to demonstrate the influence of various parameters on system performance. Finally, the paper concludes with significant findings and future research directions to apprise researchers, decision-makers, and system designers in optimizing combined charging/swapping infrastructure. © 2025 IEEE.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

Random Caching Strategy Based on Scalable Video Coding: Content Placement and Delivery in Multi-Tier Heterogeneous Networks

引用

IEEE Transactions on Communications 2025年

作者： Zhang, Xuewei Ren, Yuan Wang, Junxuan Jiang, Fan Ni, Wei Jamalipour, Abbas School of Communications and Information Engineering Xi'an710121 China Commonwealth Scientific and Industrial Research Data61 SydneyNSW2122 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

This paper proposes a new joint random caching and hierarchical transmission scheme for delivering multimedia content in cache-assisted heterogeneous networks. We use scalable video coding (SVC) and wireless edge caching to offer personalized video-watching services to end users. Resorting to stochastic geometry, we derive new expressions for successful transmission probabilities (STPs). Using the derived STPs, the achievable transmission rates (ATRs) and transmission delay experienced by multimedia users are obtained. The Gaussian-Chebyshev quadrature is employed to approximate the STPs and ATRs in closed form to improve mathematical tractability. We also attain the user satisfaction index (USI) concerning user's delay experience. A transmission delay minimization problem is formulated and solved efficiently using the standard gradient projection approach to determine the optimal layer placement design under a random caching criterion. Simulation results confirm the correctness of the derived STPs, show the negligible approximation loss of Gaussian-Chebyshev quadratures, and manifest the performance superiority of the devised SVC-based caching design to the energy-optimal caching and the non-caching approach. © 2025 IEEE.

关键词： Scalable video coding

来源：评论

学校读者我要写书评

暂无评论

Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

引用

IEEE Transactions on Mobile Computing 2025年

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Logic System-Based Decision Systems for Proactive Cybersecurity Risk Management 6th

Fuzzy Logic System-Based Decision Systems for Proactive Cybe...

引用

6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Mahadik, Ramchandra Vasant Khetani, Vinit Afaq, Ahmar Pradhan, Sagar Kolekar, V.K. Pawar, Rohit Institute of Management and Entrepreneurship Development Bharati Vidyapeeth (Deemed to Be University) Maharashtra Pune India Cybrix Technologies Maharashtra Nagpur India Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Arya College of Engineering Jaipur India Department of Computer Engineering VIIT Pune India Department of Computer Science and Engineering (Data Science) Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819619801

Proactive risk management is crucial in the ever-changing field of cybersecurity to prevent advanced threats. This study presents an innovative approach to improve cybersecurity decision-making systems by combining fuzzy logic and Random Forest, resulting in an exceptional accuracy of 98.33%. The hybrid approach aims to tackle the challenges of uncertainty and imprecision in cybersecurity data by offering a clear and strong decision-making framework. The beginning of this paper highlights the increasing cybersecurity threats and the shortcomings of conventional risk management methods. The integration of advanced decision systems is crucial as the paradigm moves toward proactive strategies. Fuzzy logic, known for its capability to manage imprecise data, is combined with the ensemble learning strength of Random Forest to form a thorough and efficient solution. The novel hybrid approach starts by converting input features into fuzzy values to represent the inherent uncertainty in cybersecurity data. A fuzzy inference system is used to convert fuzzy inputs into precise decisions, which serve as the basis for training the Random Forest model. The hybrid system effectively utilizes the ensemble features of Random Forest to identify intricate patterns in the data, resulting in an impressive accuracy of 98.33%. This research enhances cybersecurity decision systems and highlights the interpretability of the hybrid model. The system combines the clarity of fuzzy logic with the strength of Random Forest to provide a transparent view of decision-making processes and maintain a high level of predictive precision. The results highlight the efficacy of the hybrid approach in proactive cybersecurity risk management, showing its ability to greatly enhance organizational defenses against changing cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity Decision systems Fuzzy logic Proactive risk management Random Forest

来源：评论

学校读者我要写书评

暂无评论

BDGAN: Boundary and Diversity-aware Generative Adversarial Network for Imbalanced Medical Image Augmentation

BDGAN: Boundary and Diversity-aware Generative Adversarial N...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongwei Ding Qi Tao Nana Huang School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Data Science & Engineering South China Normal University Guangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current deep learning-based medical image classification methods face challenges in effectively learning correct classification boundaries when dealing with imbalanced data. Traditional data augmentation methods often suffer from insufficient diversity, leading to limited performance improvement. Based on this, this paper proposes a Boundary and Diversity-aware Generative Adversarial Network (BDGAN) for data augmentation, focusing on class boundaries and intra-class diversity. First, to enhance the diversity of generated samples, we design a multi-generator GAN architecture, where each generator learns and generates different data patterns. Second, to further generate more diverse and higher-quality samples, we introduce mutual exclusion loss and Hausdorff loss. Finally, for downstream classification tasks, we design a sampling method based on One-Class SVM (OCS), enabling the GAN to focus more on training and generating boundary samples during the training process. Experimental results on two real-world medical image datasets demonstrate that the proposed method can generate more diverse and higher-quality augmented samples, effectively improving the performance of downstream classification tasks.

关键词： Training Support vector machines Signal processing Generative adversarial networks data augmentation Sampling methods Generators Speech processing Biomedical imaging Image classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：