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Published by Taylor & Francis Group, 2022
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ISBN 10: 0367504936 ISBN 13: 9780367504939
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Published by Taylor & Francis Group, 2022
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Published by Taylor & Francis Ltd, 2022
ISBN 10: 0367504936 ISBN 13: 9780367504939
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Published by CRC Press 2022-05-15, 2022
ISBN 10: 0367504936 ISBN 13: 9780367504939
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Published by Springer-Nature New York Inc, 2021
ISBN 10: 3030338223 ISBN 13: 9783030338220
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Taschenbuch. Condition: Neu. Nature Inspired Computing for Data Science | Minakhi Rout (u. a.) | Taschenbuch | xii | Englisch | 2021 | Springer | EAN 9783030338220 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Published by CRC Press 2020-11-19, 2020
ISBN 10: 0367503360 ISBN 13: 9780367503369
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Published by Springer International Publishing, 2021
ISBN 10: 3030338223 ISBN 13: 9783030338220
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
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Published by Springer International Publishing, 2020
ISBN 10: 3030338193 ISBN 13: 9783030338190
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
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Published by Taylor & Francis Group, 2020
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