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Titlebook: Evolutionary Computing and Mobile Sustainable Networks; Proceedings of ICECM V. Suma,Noureddine Bouhmala,Haoxiang Wang Conference proceedin

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发表于 2025-3-21 17:12:04 | 显示全部楼层 |阅读模式
书目名称Evolutionary Computing and Mobile Sustainable Networks
副标题Proceedings of ICECM
编辑V. Suma,Noureddine Bouhmala,Haoxiang Wang
视频video
概述Gathers the most outstanding research papers presented at ICECMSN 2020.Discusses new findings in evolutionary computing and mobile sustainable networks.Serves as a reference resource for practitioners
丛书名称Lecture Notes on Data Engineering and Communications Technologies
图书封面Titlebook: Evolutionary Computing and Mobile Sustainable Networks; Proceedings of ICECM V. Suma,Noureddine Bouhmala,Haoxiang Wang Conference proceedin
描述.This book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20–21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks..
出版日期Conference proceedings 2021
关键词Genetic Algorithms; Optimization Algorithms; Capsule Networks; Software Defined Networking; Sustainable
版次1
doihttps://doi.org/10.1007/978-981-15-5258-8
isbn_softcover978-981-15-5260-1
isbn_ebook978-981-15-5258-8Series ISSN 2367-4512 Series E-ISSN 2367-4520
issn_series 2367-4512
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

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2367-4512 are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks..978-981-15-5260-1978-981-15-5258-8Series ISSN 2367-4512 Series E-ISSN 2367-4520
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https://doi.org/10.1057/9780230285590re is no need for any complicated preprocessing and feature extraction steps. FGNET and MORPH II datasets are used for testing the performance of the system. All these techniques outperform available state-of-the-art methods in the Rank-1 recognition rate.
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Jerome S. Stromberg,R. Galán Moreraataset considered for experimentation is a binary classification problem “Survival prediction of Titanic Passengers”. Experimental results show that training the model with the quality dataset has improved the accuracy as compared to the case when the model was trained with a raw data.
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Verification of the Model Evaluation of LSM,ementation of adders are provided. The proposed circuit designs and results are compared and implemented using a cadence software tool and for quantum circuits QCAD tools are used. The study shows the optimization in case of power, area and speed achieved in comparison with conventional circuits.
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