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Titlebook: 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022); Volume 2 R. A. Aliev,Nodirbek Rustambekovich Yusupbekov,S

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發(fā)表于 2025-3-21 16:52:23 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022)
期刊簡稱Volume 2
影響因子2023R. A. Aliev,Nodirbek Rustambekovich Yusupbekov,S.
視頻videohttp://file.papertrans.cn/101/100294/100294.mp4
發(fā)行地址Presents Advances in Intelligent System for Industrial Automation.Contains volume 2 of the Proceedings of the 12th World Conference on Intelligent System for Industrial Automation.Written by experts i
學科分類Lecture Notes in Networks and Systems
圖書封面Titlebook: 12th World Conference “Intelligent System for Industrial Automation” (WCIS-2022); Volume 2 R. A. Aliev,Nodirbek Rustambekovich Yusupbekov,S
影響因子This book presents the proceedings of the 12th World Conference "Intelligent systems for industrial automation", WCIS-2022 held in Tashkent, Uzbekistan, on November 25-26, 2022. It includes contributions from diverse areas of intelligent industrial systems design, intelligent information systems, decision making under imperfect information and others.The topics of the papers include hybrid control systems, pattern recognition, industry 4.0, information security, neural computing, fuzzy computation,?decision making and support systems, and others..
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Lecture Notes in Networks and Systemshttp://image.papertrans.cn/012/image/100294.jpg
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https://doi.org/10.1007/978-3-663-05124-4ssword, privacy, and even properties. This attack poses a great threat to inexperienced Internet users and is becoming more and more difficult. Many proposals for detecting phishing websites have shown their effectiveness and the advantage of methods for detecting and classifying Uniform Resource Lo
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https://doi.org/10.1007/978-3-663-05124-4 Because of their low elevation and proximity to frequent windstorms, coastal communities are particularly more susceptible to natural disasters. The current study uses 40?years of terraclimate data to examine the possibilities of employing machine learning algorithms to predict flood incidences in
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