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Titlebook: Computational Intelligence: Theories, Applications and Future Directions - Volume II; ICCI-2017 Nishchal K. Verma,A. K. Ghosh Conference pr

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书目名称Computational Intelligence: Theories, Applications and Future Directions - Volume II
副标题ICCI-2017
编辑Nishchal K. Verma,A. K. Ghosh
视频videohttp://file.papertrans.cn/233/232564/232564.mp4
概述Presents the latest work in the area of computational intelligence.Addresses challenges and directions for the future.Includes contributions from international researchers
丛书名称Advances in Intelligent Systems and Computing
图书封面Titlebook: Computational Intelligence: Theories, Applications and Future Directions - Volume II; ICCI-2017 Nishchal K. Verma,A. K. Ghosh Conference pr
描述.This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike..
出版日期Conference proceedings 2019
关键词Big Data and Knowledge Discovery; Data Mining and Visualization; Computer Vision; Image Processing and
版次1
doihttps://doi.org/10.1007/978-981-13-1135-2
isbn_softcover978-981-13-1134-5
isbn_ebook978-981-13-1135-2Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightSpringer Nature Singapore Pte Ltd. 2019
The information of publication is updating

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Abby Ghobadian,Nicholas O’Regan,Howard Vineylogies represent the documents using the vector space model (VSM) traditionally known as bag-of-words (BoW) hypothesis. The main disadvantage of BoW is that the grammatical and the structural information of words is not captured. In this paper, we have attempted to cluster named entities (NEs) extra
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https://doi.org/10.1057/9781137382054 detection, and many other tasks. The data generated in all these areas are very large, and there are a large number of samples along with a large number of attributes. Areas like bioinformatics have a large amount of data, but face the problem of a small number of samples with a large number of att
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Normal Morse Data of Two Morse Functionscial network can share their content. They also facilitate their users by recommending new friends on the basis of local or global network features. Local feature-based approaches do not exploit the whole network structure. In contrary to the techniques based on local features, global feature-based
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