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Titlebook: Handbook on Decision Making; Volume 3: Trends and Julian Andres Zapata-Cortes,Cuauhtémoc Sánchez-Ram Book 2023 The Editor(s) (if applicable

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書(shū)目名稱Handbook on Decision Making
副標(biāo)題Volume 3: Trends and
編輯Julian Andres Zapata-Cortes,Cuauhtémoc Sánchez-Ram
視頻videohttp://file.papertrans.cn/423/422480/422480.mp4
概述Presents recent trends and challenges in intelligent decision support systems.Presents theoretical foundations, models, experiments, and case studies for intelligent systems decision-making.Written by
叢書(shū)名稱Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Handbook on Decision Making; Volume 3: Trends and Julian Andres Zapata-Cortes,Cuauhtémoc Sánchez-Ram Book 2023 The Editor(s) (if applicable
描述.This book presents different techniques and methodologies used to improve the intelligent decision-making process and increase the likelihood of success in companies of different sectors such as Financial Services, Education, Supply Chain, Energy Systems, Health Services, and others...?..The book contains and consolidates innovative and high-quality research contributions regarding the implementation of techniques and methodologies applied in different sectors. The scope is to disseminate current trends knowledge in the implementation of artificial intelligence techniques and methodologies in different fields such as: Logistics, Software Development, Big Data, Internet of Things, Simulation, among others. The book contents are useful for Ph.D. researchers, Ph.D. students, master and undergraduate students of different areas such as Industrial Engineering, Computer Science, Information Systems, Data Analytics, and others..
出版日期Book 2023
關(guān)鍵詞Intelligent Systems; Intelligent Decision Support Systems; Decision Support; Decision Support Systems; D
版次1
doihttps://doi.org/10.1007/978-3-031-08246-7
isbn_softcover978-3-031-08248-1
isbn_ebook978-3-031-08246-7Series 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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Evaluation of Machine Learning Techniques for Malware Detectionsults available in state-of-the-art. The extreme learning machine (ELM) algorithm obtains very acceptable results in experimentation. The results successfully validate the effectiveness of the implemented algorithms, improving the generalization performance of detecting a new instance of malicious s
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