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Titlebook: Computational Intelligence, Cyber Security and Computational Models; Proceedings of ICC3, G. Sai Sundara Krishnan,R. Anitha,Manuel Gra?a Co

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發(fā)表于 2025-3-21 19:52:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computational Intelligence, Cyber Security and Computational Models
副標(biāo)題Proceedings of ICC3,
編輯G. Sai Sundara Krishnan,R. Anitha,Manuel Gra?a
視頻videohttp://file.papertrans.cn/233/232549/232549.mp4
概述Provides information on recent research trends in areas of computational intelligence, cyber security, and computational models.Includes theories and applications to provide design, analysis, and mode
叢書(shū)名稱Advances in Intelligent Systems and Computing
圖書(shū)封面Titlebook: Computational Intelligence, Cyber Security and Computational Models; Proceedings of ICC3, G. Sai Sundara Krishnan,R. Anitha,Manuel Gra?a Co
描述This book contains cutting-edge research material presented by researchers, engineers, developers, and practitioners from academia and industry at the International Conference on Computational Intelligence, Cyber Security and Computational Models (ICC3) organized by PSG College of Technology, Coimbatore, India during December 19–21, 2013. The materials in the book include theory and applications to provide design, analysis, and modeling of the key areas. The book will be useful material for students, researchers, professionals, as well academicians in understanding current research trends and findings and future scope of research in computational intelligence, cyber security, and computational models.
出版日期Conference proceedings 2014
關(guān)鍵詞Bio-Inspired Computing; Cloud Computing; Cloud Security; Combinatorial Optimization; Computational Intel
版次1
doihttps://doi.org/10.1007/978-81-322-1680-3
isbn_softcover978-81-322-1679-7
isbn_ebook978-81-322-1680-3Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer India 2014
The information of publication is updating

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PR-Evaluation in Wissenschaft und Praxis,epends on the prevalence of the disease in the immediate neighborhood. One approach to handling such linked data is “collective learning.” In collective learning, one deals with a set of data points taken at a time. The dependencies between the data points are modeled as a graph, with the nodes repr
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,Modell für strategische PR-Evaluation,In the context of the famous Internet file size data of Crovella and some very recent data sets from a wireless mobility network, we examine the new class of LogPH distributions introduced by Ramaswami for modeling heavy-tailed random variables. The fits obtained are validated using separate trainin
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,Modell für strategische PR-Evaluation,ive of this paper is to identify the optimal subset of genes from the leukemia dataset in order to classify the leukemia cancer. Different clustering approaches such as .-means (KM) clustering, fuzzy .-means (FCM) clustering, and modified .-means (MKM) clustering have been adopted in this research.
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,Instrument für strategische PR-Evaluation,tion of roadside units (RSU) that takes part in communication between vehicular nodes is a challenging task because of the high mobility or dynamic nature of these networks. The nodes in the VANET whether stationary or moving are limited in terms of storage capacity, reliability, and energy. The mai
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https://doi.org/10.1007/978-3-322-99607-7on in the presence of simultaneous sources of drift, other than sensor drift, should also be investigated, since it is often the case in practical situations. ELM is a competitive machine learning technique, which has been applied in different domains for classification. In this paper, ELM with diff
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