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Titlebook: Handling Uncertainty in Artificial Intelligence; Jyotismita Chaki Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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发表于 2025-3-21 18:48:08 | 显示全部楼层 |阅读模式
书目名称Handling Uncertainty in Artificial Intelligence
编辑Jyotismita Chaki
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概述Demonstrates different numeric and symbolic methods of handling uncertainty in artificial intelligence.Highlights on making decisions under uncertain situation.Includes examples of real-life uncertain
丛书名称SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: Handling Uncertainty in Artificial Intelligence;  Jyotismita Chaki Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi
描述.This book demonstrates different methods (as well as real-life examples) of handling uncertainty like probability and Bayesian theory, Dempster-Shafer theory, certainty factor and evidential reasoning, fuzzy logic-based approach, utility theory and expected utility theory. At the end, highlights will be on the use of these methods which can help to make decisions under uncertain situations. This book assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation. The book is comprehensive, but it prohibits unnecessary mathematics..
出版日期Book 2023
关键词Dempster-Shafer Theory; Certainty factor and evidential reasoning; Cohen‘s theory of endorsements; Fuzz
版次1
doihttps://doi.org/10.1007/978-981-99-5333-2
isbn_softcover978-981-99-5332-5
isbn_ebook978-981-99-5333-2Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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,The Dempster–Shafer Theory to Handle Uncertainty in Artificial Intelligence, This chapter discusses the basic terms used, the main components of the Dempster–Shafer theory, the Dempster–Shafer rule of combination, various case studies, and the advantages and limitations of the Dempster–Shafer theory.
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Applications of Different Methods to Handle Uncertainty in Artificial Intelligence,sorder diagnosis, prognosis, therapy, and prevention while accounting for uncertainty and variability in symptoms, tests, outcomes, and treatments. Finally, social media analysis necessitates systems that can reason about social media users’ opinions, feelings, preferences, and behaviors while deali
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Jyotismita Chakimagnetotransport in those lattices are strongly suppressed. At fields of several Tesla we enter the regime of the quantum Hall effect in the 2DEG, and we find a pronounced increase of the amplitude of 1/.-periodic magnetoresistance oscillations. The latter can be traced to an enhanced backscattering
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Jyotismita Chakistructure splitting. Across the whole range of size variations within our multimodal quantum dot distribution a systematic trend from +520µeV to −80µeV is found for decreasing dot size. To identify the underlying effects calculations of the fine-structure splitting are performed. A systematic variat
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