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Titlebook: Exploration of Novel Intelligent Optimization Algorithms; 12th International S Kangshun Li,Yong Liu,Wenxiang Wang Conference proceedings 20

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書目名稱Exploration of Novel Intelligent Optimization Algorithms
副標(biāo)題12th International S
編輯Kangshun Li,Yong Liu,Wenxiang Wang
視頻videohttp://file.papertrans.cn/320/319392/319392.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Exploration of Novel Intelligent Optimization Algorithms; 12th International S Kangshun Li,Yong Liu,Wenxiang Wang Conference proceedings 20
描述This book constitutes the refereed proceedings of the 12th International Symposium, ISICA 2021, held in Guangzhou, China, during November 19–21, 2021.??.The 48 full papers included in this book were carefully reviewed and selected from 99 submissions. They were organized in topical sections as follows: new frontier of multi-objective evolutionary algorithms; intelligent multi-media; data modeling and application of artificial intelligence; exploration of novel intelligent optimization algorithm; and intelligent application of industrial production..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer systems; computer vision; correla
版次1
doihttps://doi.org/10.1007/978-981-19-4109-2
isbn_softcover978-981-19-4108-5
isbn_ebook978-981-19-4109-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2022
The information of publication is updating

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A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differeion and authentication mechanism, the vehicle network is vulnerable to malicious attacks and intrusion, which poses a threat to the driver’s life safety and data privacy. To solve this problem, this paper proposes a method combining federal learning and differential privacy protection to protect the
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The Combination of?GRU and?Dense Block for?Image Denoising Network) and Dense block. The GRU contacts the previous state through the current state, which can more effectively extract noise features from the picture. The Dense block can effectively improve the feature propagation efficiency, reduce the problem of vanishing gradient, and optimize the process of feat
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