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Titlebook: Machine Learning Approaches in Financial Analytics; Leandros A. Maglaras,Sonali Das,Srikanta Patnaik Book 2024 The Editor(s) (if applicabl

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發(fā)表于 2025-3-21 19:57:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning Approaches in Financial Analytics
編輯Leandros A. Maglaras,Sonali Das,Srikanta Patnaik
視頻videohttp://file.papertrans.cn/621/620388/620388.mp4
概述Covers all aspects of financial markets along with effective exploration and exploitation of machine learning techniques.Presents recent research on machine learning approaches in Financial Analytics.
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Machine Learning Approaches in Financial Analytics;  Leandros A. Maglaras,Sonali Das,Srikanta Patnaik Book 2024 The Editor(s) (if applicabl
描述.This book addresses the growing need for a comprehensive guide to the application of machine learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and data science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of machine learning in the financial sector responsibly..
出版日期Book 2024
關(guān)鍵詞Fuzzy Sets, Rough Sets; Granular Computing for Financial Application; Evolutionary Computation for Fin
版次1
doihttps://doi.org/10.1007/978-3-031-61037-0
isbn_softcover978-3-031-61039-4
isbn_ebook978-3-031-61037-0Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
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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978-3-031-61039-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Machine Learning Approaches in Financial Analytics978-3-031-61037-0Series ISSN 1868-4394 Series E-ISSN 1868-4408
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Fully Homomorphic Encrypted Wavelet Neural Network for Privacy-Preserving Bankruptcy Prediction in Biciency of the model. We tested the effectiveness of the proposed method on four datasets taken from the finance domain especially on the bankruptcy prediction. The results show that our proposed model performs similarly to or better than the unencrypted model on the datasets.
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