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Titlebook: Key Digital Trends Shaping the Future of Information and Management Science; Proceedings of 5th I Lalit Garg,Dilip Singh Sisodia,Deepak Sin

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书目名称Key Digital Trends Shaping the Future of Information and Management Science
副标题Proceedings of 5th I
编辑Lalit Garg,Dilip Singh Sisodia,Deepak Singh
视频videohttp://file.papertrans.cn/543/542556/542556.mp4
概述Includes recent research on Information Systems and Management Science.Presents the proceedings of the ISMS’22 Conference held in Msida, Malta, at October 6–9, 2022.Written by experts in the field
丛书名称Lecture Notes in Networks and Systems
图书封面Titlebook: Key Digital Trends Shaping the Future of Information and Management Science; Proceedings of 5th I Lalit Garg,Dilip Singh Sisodia,Deepak Sin
描述.This book  (proceedings of ISMS 2022)   is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of information systems and management science. This textbook shows how to exploit information systems in a technology-rich management field. The book introduces concepts, principles, methods, and procedures that will be valuable to students and scholars in thinking about existing organization systems, proposing new systems, and working with management professionals in implementing new information systems..
出版日期Conference proceedings 2023
关键词Information Systems; Management Science; ISMS 2022; Data Acquisition; Knowledge Acquisition; Strategic Pl
版次1
doihttps://doi.org/10.1007/978-3-031-31153-6
isbn_softcover978-3-031-31152-9
isbn_ebook978-3-031-31153-6Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Improvisation of Predictive Modeling Using Different Classifiers for Predicting Thyroid Disease in the mis-classification rate is nearly negligible. The main aim of study is to explore the various classification algorithms like k-NN, Random Forest Classifier, XGBoost Classifier and CatBoost Classifier and improvise the models using hyper-parameter optimization to obtain optimum accuracy in predi
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