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Titlebook: Information, Communication and Computing Technology; 8th International Co Jemal Abawajy,Jo?o Manuel R.S. Tavares,Ali Bou Nas Conference pro

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發(fā)表于 2025-3-21 16:57:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Information, Communication and Computing Technology
副標(biāo)題8th International Co
編輯Jemal Abawajy,Jo?o Manuel R.S. Tavares,Ali Bou Nas
視頻videohttp://file.papertrans.cn/466/466000/466000.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Information, Communication and Computing Technology; 8th International Co Jemal Abawajy,Jo?o Manuel R.S. Tavares,Ali Bou Nas Conference pro
描述This book constitutes the refereed proceedings of the 8th International Conference?on?Information, Communication and Computing Technology, ICICCT 2023, held in New Delhi, India, during?May 27, 2023..The 14 full papers included in this book were carefully reviewed and?selected from 60 submissions. They were organized in topical sections as follows:?global platform for researchers, scientists and practitioners from both academia and industry to present their research and development activities in all the aspects of?Pattern Recognition?and computational Intelligence techniques..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; machine learning; computer security; pattern recognition; security of data; clus
版次1
doihttps://doi.org/10.1007/978-3-031-43838-7
isbn_softcover978-3-031-43837-0
isbn_ebook978-3-031-43838-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Information, Communication and Computing Technology978-3-031-43838-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
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https://doi.org/10.1007/978-3-031-43838-7artificial intelligence; machine learning; computer security; pattern recognition; security of data; clus
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An Empirical Study of Intrusion Detection by Combining Clustering and Classification Methodsachine learning and artificial intelligence applications are used for this task. Nevertheless, these models were claimed to operate better when the data was clustered before the classification process. In this work, we study the effect of clustering datasets related to attacks applied in different n
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