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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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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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DEFER: distributed Edge Inference for Deep neural networks 14

DEFER: Distributed Edge Inference for Deep Neural Networks

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14th International conference on COMmunication Systems and networkS, COMSNETS 2022

作者： Parthasarathy, Arjun Krishnamachari, Bhaskar Crystal Springs Uplands School United States University of Southern California United States

ISBN: (纸本)9781665421041

Modern machine learning tools such as deep neural networks (DNNs) are playing a revolutionary role in many fields such as natural language processing, computer vision, and the internet of things. Once they are trained, deep learning models can be deployed on edge computers to perform classification and prediction on real-time data for these applications. Particularly for large models, the limited computational and memory resources on a single edge device can become the throughput bottleneck for an inference pipeline. To increase throughput and decrease per-device compute load, we present DEFER (distributed Edge inFERence), a framework for distributed edge inference, which partitions deep neural networks into layers that can be spread across multiple compute nodes. The architecture consists of a single 'dispatcher' node to distribute DNN partitions and inference data to respective compute nodes. The compute nodes are connected in a series pattern where each node's computed result is relayed to the subsequent node. The result is then returned to the Dispatcher. We quantify the throughput, energy consumption, network payload, and overhead for our framework under realistic network conditions using the CORE network emulator. We find that for the ResNet50 model, the inference throughput of DEFER with 8 compute nodes is 53% higher and per node energy consumption is 63 % lower than single device inference. We further reduce network communication demands and energy consumption using the ZFP serialization and LZ4 compression algorithms. We have implemented DEFER in Python using the TensorFlow and Keras ML libraries, and have released DEFER as an open-source framework to benefit the research community. © 2022 IEEE.

关键词： Deep neural networks

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Predicting AGV Battery Cell Voltage Using a neural network Approach with Preliminary Data Analysis and processing

Predicting AGV Battery Cell Voltage Using a Neural Network A...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Pavliuk, Olena Medykovskyy, Mykola Steclik, Tomasz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Lviv Polytechnic National University Automated Control Systems Department Lviv Ukraine

ISBN: (纸本)9798350324457

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. Five filters were selected for smoothing: Moving average;Weighted moving average;Exponential smoothing;Smoothing with local regression;Lowess smoothing. RNN forecasting was performed on the basis of a centered stationary signal obtained by subtracting the value obtained with the five filters from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, MBE, SEBIAS, ME. Adequacy of the proposed neural network model based on the determination coefficient R2. The coefficient of determination for all the centered signals obtained using the above five methods yielded adequate predictive models. Exponential smoothing (MAPE=0.002;RMSE=141.481;R2=0.94) and LS (MAPE=0.002;RMSE=124.239;R2:0.96) proved to be the most effective methods for short-term forecasts. Moving average (MAPE=0.00207;RMSE=146.589;R2=0.94) is recommended for long-term forecasts with additional smoothing of the forecasted values because of the noise in them. © 2023 IEEE.

关键词： AGV battery cell voltage data pre-processing forecast Machine Learning

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Deep Learning-Based Detection of Seedling Development from Indoor to Outdoor 28th

Deep Learning-Based Detection of Seedling Development from I...

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28th International conference on Systems, Signals and Image processing (IWSSIP)

作者： Garbouge, Hadhami Rasti, Pejman Rousseau, David Univ Angers UMR INRAe IRHS LARIS 62 Ave Notre Dame du Lac F-49000 Angers France ESAIP Dept Comp Sci Angers France

ISBN: (纸本)9783030968786;9783030968779

Monitoring plant growth with computer vision is an important topic in plant science. This monitoring can be challenging when plants are located in outdoor conditions due to light variations and other noises. On other hand, there is a lack of annotated datasets available for such outdoor environments to train machine learning algorithms while indoor similar datasets may be more easily available. In this communication, we investigate, for the first time to the best of our knowledge in plant imaging, how to take benefit from model trained in fully controlled environment to build model for an outdoor environment. This is illustrated with a use case recently published for indoor conditions that we revisit and extend. We compare various spatial and spatio-temporal neural network architectures including long-short term memory convolutional neural network, time distributed convolutional neural network and transformer. While the spatio-temporal architectures outperform the spatial one in indoor conditions, the temporal information appears to be degraded by the presence of shadows due to the variation of light in outdoor conditions. We introduce a specific data augmentation and transfer learning approach which enables to reach a performance of 91% of good classifications with very limited effort of annotation.

关键词： Plant phenotyping Deep learning Transfer learning Data augmentation CNN-LSTM Time distributed deep learning Transformers

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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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3rd International conference on distributed Sensing and Intelligent Systems, ICDSIS 2022

3rd International Conference on Distributed Sensing and Inte...

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3rd International conference on distributed Sensing and Intelligent Systems, ICDSIS 2022

ISBN: (纸本)9781839538186

The proceedings contain 34 papers. The topics discussed include: prototyping an environmental sensor network for meteorological and climate studies at the city scale;intelligent system for governing risks in smart cities applications;sleep apnea detecting based on ECG and EEG signals using discrete wavelet transform and artificial neural network;forecasting Dubai City water demand using the fuzzy logic approach;analyzing domestic energy behavior with a multi-dimensional appliance-level dataset;TOPSIS multi-criteria decision-making method for mechanical energy storage systems' evaluation;detection of Alzheimer’s disease using machine learning models: the impact of pre-processing;epileptic seizure detection using short time Fourier transform and convolutional neural network;extended state observer-based neural networks with super-twisting sliding mode control feedback stabilizer;and Arabic sign language recognition algorithm based on deep learning for smart cities.

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DEDF: An Enhanced Differential Evolution Algorithm with Dynamic-selection Framework in IIOT 29

DEDF: An Enhanced Differential Evolution Algorithm with Dyna...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Zhou, Zhou Fangmin Li, U. Liu U, Huazhong Changsha University School of Computer Science and Engineering Changsha China Hainan University School of Computer Science and Technology Haikou China

ISBN: (纸本)9798350330717

To solve the problems of slow convergence and limited prediction ability of the Differential Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic-selection framework (denoted by DEDF) is proposed. DEDF develops a trusted framework containing five mutation strategies to realize the dynamic selection of mutation strategies. On the basis of the framework, the mutation factor, crossover factor, and local exit strategy are improved to balance the algorithm's local search and global search ability. A series of tests on the function set CEC2017 has been performed, and the findings show that compared with other benchmark algorithms, the DEDF has advantages in convergence speed and accuracy. The proposed algorithm DEDF can be effectively leveraged to address multi-objective optimization issues in IIOT. © 2023 IEEE.

关键词： differential evolution IIOT neural network resource optimization trustworthiness of dynamic-selection framework

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Secured Data Sharing of Medical Images for Disease diagnosis using Deep Learning Models and Federated Learning Framework

Secured Data Sharing of Medical Images for Disease diagnosis...

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2023 International conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Anusuya, R. Karthika Renuka, D. Oviya, S. Sangavi, R. PSG College of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350335835

Sharing data between connected and automated healthcare sectors without any protection will leak private information. Health information of patient must be carefully handled when shared between various actors in health sector like doctors, lab technicians and data scientist or model owners who executes machine learning model for disease analysis. Patient health related data like X-Ray images must be secured before disease analysis is done. We have taken Pediatric chest x-rays for pneumonia classification. The X-ray images are encrypted before classification. Our work focusses on three encryption algorithm and evaluates the performance based on execution time. After encryption a deep learning model for image classification is built with Convolutional neural network (CNN). For experimental results, same dataset is classified using VGG16 transfer learning model which shows a better performance in terms of accuracy and processing cost. We have also demonstrated that the accuracy is further enhanced when the same model is executed in federated distributed learning environment. © 2023 IEEE.

关键词： Convolutional neural networks

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EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in distributed Optimization 36

EF-BV: A Unified Theory of Error Feedback and Variance Reduc...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Condat, Laurent Yi, Kai Richtarik, Peter King Abdullah Univ Sci & Technol KAUST Thuwal 239556900 Saudi Arabia

ISBN: (纸本)9781713871088

In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce the number of bits sent within each communication round of iterative methods. There are two classes of compression operators and separate algorithms making use of them. In the case of unbiased random compressors with bounded variance (e.g., rand-k), the DIANA algorithm of Mishchenko et al. (2019), which implements a variance reduction technique for handling the variance introduced by compression, is the current state of the art. In the case of biased and contractive compressors (e.g., top-k), the EF21 algorithm of Richtarik et al. (2021), which instead implements an error-feedback mechanism, is the current state of the art. These two classes of compression schemes and algorithms are distinct, with different analyses and proof techniques. In this paper, we unify them into a single framework and propose a new algorithm, recovering DIANA and EF21 as particular cases. Our general approach works with a new, larger class of compressors, which has two parameters, the bias and the variance, and includes unbiased and biased compressors as particular cases. This allows us to inherit the best of the two worlds: like EF21 and unlike DIANA, biased compressors, like top-k, whose good performance in practice is recognized, can be used. And like DIANA and unlike EF21, independent randomness at the compressors allows to mitigate the effects of compression, with the convergence rate improving when the number of parallel workers is large. This is the first time that an algorithm with all these features is proposed. We prove its linear convergence under certain conditions. Our approach takes a step towards better understanding of two so-far distinct worlds of communication-efficient distributed learning.

关键词： Compressors

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