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Hadoop neural network for parallel and distributed feature selection

neural networkS 2016年 78卷 24-35页

作者： Hodge, Victoria J. O'Keefe, Simon Austin, Jim Univ York Dept Comp Sci Adv Comp Architecture Grp York YO10 5GH N Yorkshire England

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license.

关键词： Hadoop MapReduce distributed Parallel Feature selection Binary neural network

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A Discrete-Time Collaborative Neurodynamic Approach to distributed Global Optimization 13

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

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13th International Conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun 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 HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

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. © 2025 IEEE.

关键词： neural network models

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Empirical Study on Myopia Identification Using CNN Hereditary Modelfor Resource Constrained Ophthalmology

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International Journal Bioautomation 2025年第1期29卷 19-32页

作者： Nazifa, Aqila Joshi, Manisha Shivaram Ramani, Soumya Medical Electronics Engineering Department BMS College of Engineering Karnataka Bengaluru560019 India Ophthalmology Department Ramaiah Memorial Hospital Karnataka Bengaluru560019 India

Refractive errors, which include myopia, hyperopia, presbyopia, and astigmatism, are common vision problems that result in blurred vision when light rays are not focused correctly on the retinal plane. Diagnosis and classification of refractive errors are essential for providing appropriate corrective measures such as glasses or contact lenses. The key objective of this research is to establish an efficient and fast approach to identifying a refractive defect and categorizing them. Leveraging the capabilities of modern technology, we utilize a smartphone’s camera to capture pictures of the red reflex in the eye. During capturing, the photos are processed using recent image processing techniques to identify any irregularities or asymmetries that may indicate refractive errors. By comparing our method to other current models, we hope to illustrate the advantage of our Hereditary model, which combines a random forest and a convolutional neural network, in accurately diagnosing and classifying refractive errors. Additionally, the proposed approach can serve as a foundation in order to do additional research and development in machine learning and image processing methods improvements for the classification of ocular disorders. © 2025 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/).

关键词： Convolutional neural networks

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An Efficient and Generalized Auto-Design Methodology for Complicated Beamforming networks

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年第5期73卷 2776-2791页

作者： Liu, Anlan Yang, Yimin Yu, Ming Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab EM Informat Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

To deal with different beamforming network (BFN) design tasks, a generalized auto-design methodology is developed by the following three steps. First, a structure reconfiguration is performed by using a binary design matrix assisted with different reconfiguration functions. Then, to speed up the component design, the homotopy continuation (HC) method is exploited to form a partial database. A mapping technique ensures the partial database to be expanded to the complete database, which covers the whole feature space. An inverse model is well trained based on the complete database to generate the physical parameters of multiple couplers. Finally, the optimal distributed phase compensation is decided by minimizing the total phase shift value of phase shifters. For the demonstration, two different waveguide BFNs are designed. In addition, this work discussed the potential of using the proposed methodology to design BFNs with a more complex configuration or BFNs in other materials. The results prove that the proposed methodology allows an automatic and simultaneous generation of multiple physical structures, which can improve the BFN's electrical performance or meet different physical specifications (PS). Considering many possible problems in a BFN design, the proposed methodology is generalized to different classes of BFNs. Moreover, the component design can be finished within a shorter time than existing algorithms.

关键词： Couplers Topology Transmission line matrix methods Databases network topology Butler matrices Matrix decomposition Artificial neural networks Array signal processing Artificial neural network (ANN) beamforming network (BFN) computer-aided design (CAD) design automation directional coupler homotopy continuation (HC) phase shifter (PSF) phase shifter (PSF)

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Unsupervised Deep Learning for DAS-VSP Denoising Using Attention-Based Deep Image Prior

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Cui, Yang Bin Waheed, Umair Chen, Yangkang King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia Univ Texas Austin John A & Katherine G Jackson Sch Geosci Bur Econ Geol Austin TX 78712 USA

distributed acoustic sensing (DAS) has emerged as a widely used technology in various applications, including borehole microseismic monitoring, active source exploration, and ambient noise tomography. Compared with conventional geophones, the fiber optic cable has unique characteristics that allow it to withstand high-temperature and high-pressure environments. However, due to its high sensitivity, the obtained seismic records are often corrupted with unavoidable background noise, which introduces more uncertainty in the subsequent seismic data processing and interpretation. Thus, the development of robust denoising techniques for DAS data is crucial to minimize the impact of noise and enhance the reliability of seismic data processing and interpretation. In this work, we propose a ground-truth-free method for strong background noise suppression in DAS vertical seismic profiling (DAS-VSP) data. Compared to existing deep learning (DL) methods, the proposed approach demonstrates promising generalizability in handling field examples across different surveys. The proposed method consists of four stages: training set extension with a patching scheme, feature selection with a kurtosis-based method, denoising with a deep image prior (DIP)-based unsupervised neural network, and an unpatching approach for denoised data reconstruction. Numerical experiments conducted on synthetic data and several profiles from the Utah FORGE project and the Gro ss Sch & ouml;nebeck site demonstrate that the proposed method can effectively suppress most of the background noise while preserving hidden signals. Furthermore, the unsupervised learning (USL) approach is unconditionally generalizable when applied to vastly different field data because it does not require pre-labeled datasets for training. The codes related to this article are fully open-source via https://***/cuiyang512/Unsupervised-DAS-Denoising.

关键词： Noise Noise reduction Training Kurtosis Feature extraction Background noise Noise measurement Training data Optical fibers Data processing Denoising distributed acoustic sensing (DAS) seismic data processing unsupervised learning (USL)

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An Event Recognition Method for a Φ-OTDR System Based on CNN-BiGRU network Model with Attention

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PHOTONICS 2025年第4期12卷 313-313页

作者： Li, Changli Chen, Xiaoyu Shi, Yi Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Shantou Univ Coll Engn Shantou 515063 Peoples R China

The phase-sensitive optical time domain reflectometry (Phi-OTDR) technique offers a method for distributed acoustic sensing (DAS) systems to detect external acoustic fluctuations and mechanical vibrations. By accurately identifying vibration events, DAS systems provide a non-invasive solution for security monitoring. However, limitations in temporal signal analysis and the lack of spatial features significantly impact classification accuracy in event recognition. To address these challenges, this paper proposes a network model for vibration-event recognition that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and attention mechanisms, referred to as CNN-BiGRU-Attention (CBA). First, the CBA model processes spatiotemporal matrices converted from raw signals, extracting low-level features through convolution and pooling. Subsequently, features are further extracted and separated along both the temporal and spatial dimensions. In the spatial-dimension branch, horizontal convolution and pooling generate enhanced spatial feature maps. In the temporal-dimension branch, vertical convolution and pooling are followed by BiGRU processing to capture dynamic changes in vibration events from both past and future contexts. Additionally, the attention mechanism focuses on extracted features in both dimensions. The features from the two dimensions are then fused using two cross-attention mechanisms. Finally, classification probabilities are output through a fully connected layer and a softmax activation function. In the experimental simulation section, the model is validated using real-world data. A comparison with four other typical models demonstrates that the proposed CBA model offers significant advantages in both recognition accuracy and robustness.

关键词： attention BiGRU CNN distributed optical fiber vibration sensing event recognition phase-sensitive optical time domain reflectometry (Phi-OTDR)

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Security and Privacy Preservation via Interference Tolerant Fast Convergence Zeroing neural network With Reptile Search Optimization Algorithm in Fog-Cloud Computing

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第4期36卷

作者： Kumar, Pakkarisamy Janakiraman Sathish Verma, Neha Gupta, Shivani Jothilakshmi, Rajendran Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India GGSIPU Vivekananda Inst Profess Studies Dept Informat Technol Delhi India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India RMD Engn Coll Dept Informat Technol Chennai Tamil Nadu India

More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing neural network for Security and Privacy Preservation with Reptile Search Optimization Algorithm in Fog-Cloud Computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then Reptile Search Optimization Algorithm (RSOA) is proposed to optimize the ITFCZNN, and Effective Lightweight Homomorphic Cryptographic Algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-Fog-Cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-awar

关键词： effective lightweight homomorphic cryptographic algorithm fog-cloud computing interference-tolerant fast convergence zeroing neural network privacy preserving reptile search optimization algorithm

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Edge-Cloud Collaborative Streaming Video Analytics With Multi-Agent Deep Reinforcement Learning

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IEEE network 2025年第1期39卷 165-173页

作者： Qian, Bin Xuan, Yubo Wu, Di Wen, Zhenyu Yang, Renyu He, Shibo Chen, Jiming Ranjan, Rajiv Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG England Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Univ St Andrews Sch Comp Sci St Andrews KY16 9AJ Scotland Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China Beihang Univ Sch Software Beijing 100191 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310058 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310058 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China

Streaming video analytics focuses on the real-time analysis of streaming video data from multiple resources, such as security cameras, and IoT devices with video capabilities. It involves applications of various techniques to extract valuable information from live video streams. Edge computing and cloud computing facilitate video stream analytics by utilizing computation resources across both ends, enabling both high accuracy and low latency. However, video streaming behaviours are dynamic and constantly evolving across the edge and the cloud. The network conditions, computing resources, and video content can change rapidly, making it crucial to continuously adjust the analytics methods to provide accurate results. Previous works both based on deep neural networks (DNNs) or heuristic algorithms learn a suitable deployment plan for streaming video analytics applications from historical data or synthetic data and therefore are not able to capture the dynamics. Hence, we propose reinforcement learning-based methods that can adapt to ongoing changes in video streaming behaviours. To ensure the scalability of video analytics in distributed environments, we implement OsmoticGate2, a distributed streaming video analytics system that features optimized processing pipelines and multi-agent RL-based controllers for fast adapting the system configurations across the edge and the cloud. Experiments on a real testbed show that our method outperforms baselines, assuring real-time video analysis and high accuracy in dynamic and distributed environments.

关键词： Streaming media Visual analytics Cloud computing Servers Streams Real-time systems Delays real-time video analytics distributed system multiagent deep reinforcement learning edge cloud collaboration

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Execution Time Optimization for Pipeline Deep network Training on Multiple GPUs 33

Execution Time Optimization for Pipeline Deep Network Traini...

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33rd Euromicro International Conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Wu, Bing-Jou Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9798331524937

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput. © 2025 IEEE.

关键词： Deep neural networks

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An Exhaustive Survey on the Methods and Applications of Graph neural networks 8th

An Exhaustive Survey on the Methods and Applications of Grap...

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8th International Conference on Information Science, Communication and Computing

作者： Fan, Dong Cheng, Jieren Tang, Xiangyan Lu, Kai Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Hainan Peoples R China Sch Cyberspace Secur Sch Cryptol Haikou 570228 Hainan Peoples R China Hainan Vocat Coll Polit & Law Dept Publ Safety Technol Haikou 571100 Hainan Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

The widespread use of deep learning has achieved unprecedented success. neural networks have achieved remarkable results in natural language processing, image recognition and other application fields. The key to the success of neural networks lies in its efficient processing of Euclidean space data, but the results obtained when traditional neural networks are used in non-Euclidean spaces are often not satisfactory to researchers. Graph neural network has been widely developed as a neural network that can be used in non-Euclidean space. This paper starts with the basic knowledge of graphs, introduces graph neural networks, and makes a new classification of them. Then it illustrates its application from 18 application areas. Finally, I put forward my own views on the future development and challenges of graph neural networks.

关键词： Graph neural networks Convolution Recurrent Graph Generation Euclidean non-Euclidean

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