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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 2988 条记录，以下是31-40 订阅

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615278

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence Computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with Parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A neural network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615506

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615445

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distributed-computing based versatile healthcare services framework for diagnostic markers 2nd

Distributed-computing based versatile healthcare services fr...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Chunduri, Venkata Posinasetty, Bhargavi Son, Mukesh Byeon, Haewon Department of Mathematics & Computer Science Indiana State University United States The University of Southern Mississippi HattiesburgMS United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune Tathawade India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

ISBN: (纸本)9781032738659

Recently, there has been a boom in imaginative medical services technologies that have enhanced the delivery of medical care data. Human-to-human and gadget-to-gadget networks are crucial in people’s lives and function 24 hours a day, seven days a week. MobileCloud-Computing becomes a crucial tool in this regard. Given the tremendous improvements in the Internet of Things, such a combination has become a major worry. This study developed a framework that included two essential application components: flexible usage and a server request. The server application records patient data while a Counterfeit neural network (CNN) module distinguishes between the two stroke subtypes. Similarly, as a framework for stroke patients, our model ensures accessibility, security, and adaptability by utilising the Stroke dataset for CNN computation and the Multilayer Perceptron Algorithm (MLP), which has been completed without precedent for working with large amounts of data in this extension. © 2025 The Author(s).

关键词： Hospital data processing

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International conference on 6G Communications networking and Signal processing, SGCNSP 2023

International Conference on 6G Communications Networking and...

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International conference on 6G Communications networking and Signal processing, SGCNSP 2023

ISBN: (纸本)9789819778751

The proceedings contain 34 papers. The special focus in this conference is on 6G Communications networking and Signal processing. The topics include: Secure Clustering and Routing Based on Modified Whale Optimization Algorithm in MANET;improved Whale Optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor networks;resource Allocation in Cloud Computing Using Quasi-Opposition Learning Based Aquila Optimizer;the Quasi-Oppositional-Based Learning Aquila Optimization Routing Protocol in Energy-Aware Wireless Communication;exponential-Weighted Ant Lion Optimization Based Clustering Algorithm for Wireless Body Sensor networks;Optimized Routing Strategy in Mobile Sink Based WSN Using Multi-objective Osprey Optimization Algorithm;the Coverage Optimization in Wireless Sensor networks Using Improved Artificial Rabbit Optimization;improved Moth Flame Optimization Based Cluster Head Selection and Data Aggregation Using Machine Learning Approach;Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN;an Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering;adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor networks;An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor networks;peak-to-Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Using Improved Butterfly Optimization Algorithm;energy Efficient-Based Cluster Head Selection and Routing Protocol for Wireless Sensor networks Using Aquila Optimizer;ensemble Artificial neural network and Support Vector Machine Based Parameter Evaluation in Wireless Sensor networks;distributed Denial of Service Attack Detection in IoT Utilizing Attention Mechanism Based Long Short-Term Memory;e-commerce Payment System Empowered by Blockchain for Enhanced Security.

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Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum

Adaptive AI-based Decentralized Resource Management in the C...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Lanpei Li Jack Bell Massimo Coppola Vincenzo Lomonaco ISTI-CNR Pisa Italy Department of Computer Science University of Pisa Pisa Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In the Cloud-Edge Continuum, dynamic infrastructure change and variable workloads complicate efficient resource management. Centralized methods can struggle to adapt, whilst purely decentralized policies lack global oversight. This paper proposes a hybrid framework using Graph neural network (GNN) embeddings and collaborative multi-agent reinforcement learning (MARL). Local agents handle neighbourhood-level decisions, and a global orchestrator coordinates system-wide. This work contributes to decentralized application placement strategies with centralized oversight, GNN integration and collaborative MARL for efficient, adaptive and scalable resource management.

关键词： Training Scalability Collaboration Reinforcement learning Organizations Dynamic scheduling Decentralized applications Graph neural networks Robustness Resource management

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A Discrete-Time Collaborative Neurodynamic Approach to distributed Global Optimization

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Haoen Huang Zhigang Zeng Jun Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science & Department of Data Science City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results.

关键词： Neurodynamics Adaptation models Adaptive systems Simulation Optimization models neural networks Collaboration Information processing Optimization Intelligent control

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Quantum-Train with Tensor network Mapping Model and distributed Circuit Ansatz

Quantum-Train with Tensor Network Mapping Model and Distribu...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen-Yu Liu Chu-Hsuan Abraham Lin Kuan-Cheng Chen Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. To address these issues, we propose replacing the MLP with a tensor network-based model and introducing a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes. This approach enhances scalability, efficiently represents high-dimensional data, and maintains a compact model structure. Our enhanced QT framework retains the benefits of reduced parameter count and independence from quantum resources during inference. Experimental results on benchmark datasets demonstrate that the tensor network-based QT framework achieves competitive performance with improved efficiency and generalization, offering a practical solution for scalable hybrid quantum-classical machine learning.

关键词： Weight measurement Tensors Quantum computing High dimensional data Scalability neural networks Signal processing Quantum state Integrated circuit modeling Speech processing

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State-Augmented Opportunistic Routing in Wireless Communication Systems with Graph neural networks

State-Augmented Opportunistic Routing in Wireless Communicat...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sourajit Das Navid NaderiAlizadeh Alejandro Ribeiro University of Pennsylvania Duke University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this study, we address the challenge of packet based information routing in large-scale wireless communication networks. We approach this scenario by framing the problem as a statistical learning problem, where each node in the network relies only on the local data. Our exploration focuses on the idea of opportunistic routing, which exploits the broadcast nature of wireless communication to select the optimal relay node and transmit information packets to the destination node via multiple relay nodes. We present a distributed optimization method based on state augmentation (SA) that aims to maximize the total information on different source nodes of the network. Our formulation of the problem deploys graph neural networks (GNNs) to perform graph convolution on the topological connections between the network nodes. Using unsupervised learning, we derive optimal routing policies for the source nodes across multiple flows from the GNN output. Numerical results show the superiority of our proposed method by comparing a GNN model trained against standard algorithms.

关键词： Wireless communication Statistical learning Signal processing algorithms Optimization methods Routing Graph neural networks Relays Speech processing Unsupervised learning Standards

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Detection of Anomalous Cryptocurrency Transactions Using neural networks with Decision-making Explanation

Detection of Anomalous Cryptocurrency Transactions Using Neu...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Dmitry Levshun Ksenia Zhernova Andrey Chechulin St. Petersburg Federal Research Center of the Russian Academy of Sciences Saint Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The objective of this paper is to explore an effective approach for detecting anomalies in cryptocurrency transactions using artificial intelligence techniques. The proposed method is compared with existing approaches for identifying illegal transactions within cryptocurrency networks. This research systematically analyzes various data processing methods applicable to the problem of detecting illicit transactions, as well as reviewing relevant studies in cryptocurrency transaction analysis, data visualization, and computer vision-based anomaly detection. Experimental evaluations on a cryptocurrency transaction dataset show significant insights, with results benchmarked against those obtained in prior studies on the same dataset (BABD). The findings suggest that the proposed method, utilizing statistical graph characteristics, deep and ensemble learning technologies, enhances the accuracy of identifying fraudulent transactions. This approach could be instrumental in developing software solutions for monitoring cryptocurrency transactions, potentially aiding in digital forensics and cybersecurity applications. The findings may benefit researchers in computer security and developers focused on information security systems.

关键词： Adaptation models Accuracy Sensitivity neural networks Digital forensics Information security Cryptocurrency Fraud Ensemble learning Anomaly detection

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