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Titlebook: Computational Intelligence in Data Mining; Proceedings of the I Himansu Sekhar Behera,Durga Prasad Mohapatra Conference proceedings 2017 Sp

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书目名称Computational Intelligence in Data Mining
副标题Proceedings of the I
编辑Himansu Sekhar Behera,Durga Prasad Mohapatra
视频video
概述Contains current research issues of developments of data mining and applications of computational intelligence methods.Provides attractive resource to meet new research challenges and problem findings
丛书名称Advances in Intelligent Systems and Computing
图书封面Titlebook: Computational Intelligence in Data Mining; Proceedings of the I Himansu Sekhar Behera,Durga Prasad Mohapatra Conference proceedings 2017 Sp
描述.The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science. .
出版日期Conference proceedings 2017
关键词Computational Intelligence; CIDM 2016; ICCIDM; Conference Proceedings; Data Mining; Fuzzy Logic; Machine L
版次1
doihttps://doi.org/10.1007/978-981-10-3874-7
isbn_softcover978-981-10-3873-0
isbn_ebook978-981-10-3874-7Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer Nature Singapore Pte Ltd. 2017
The information of publication is updating

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Strategic Management in Islamic Financeliminate the noise from noisy image, one should know the noise type, noise level, noise distribution, etc. Typically noise level information is identified from noise standard deviation. Estimation of the image noise from the noisy image is major concern for several reasons. So, efficient and effecti
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Summary and Concluding Remarks,al Foraging Optimization Algorithm (BFOA). Ordinary threading methods are computationally expensive, while extending for multilevel image thresholding, so there is a need of optimization techniques to reduce the computational time. Particle swarm optimization undergoes instability when particle velo
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