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Titlebook: Intelligence Science III; 4th IFIP TC 12 Inter Zhongzhi Shi,Mihir Chakraborty,Samarjit Kar Conference proceedings 2021 IFIP International F

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發(fā)表于 2025-3-21 19:12:08 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Intelligence Science III
副標題4th IFIP TC 12 Inter
編輯Zhongzhi Shi,Mihir Chakraborty,Samarjit Kar
視頻videohttp://file.papertrans.cn/470/469273/469273.mp4
叢書名稱IFIP Advances in Information and Communication Technology
圖書封面Titlebook: Intelligence Science III; 4th IFIP TC 12 Inter Zhongzhi Shi,Mihir Chakraborty,Samarjit Kar Conference proceedings 2021 IFIP International F
描述.This book constitutes the refereed post-conference proceedings of the 4th International Conference on Intelligence Science, ICIS 2020, held in Durgapur, India, in February 2021 (originally November 2020)...The 23 full papers and 4 short papers presented were carefully reviewed and selected from 42 submissions. One extended abstract is also included. They deal with key issues in brain cognition; uncertain theory; machine learning; data intelligence; language cognition; vision cognition; perceptual intelligence; intelligent robot; and medical artificial intelligence..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; communication systems; computer networks; computer vision; databases; fuzzy sets
版次1
doihttps://doi.org/10.1007/978-3-030-74826-5
isbn_softcover978-3-030-74828-9
isbn_ebook978-3-030-74826-5Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2021
The information of publication is updating

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Hexagon of Intelligenceo characterize what intelligence is, its relation with computability, creativity, understanding and undecidability..In a first part, we make some general comments about the history, development and objectives of AI. In a second part, we present two diametrically opposed ways of reasoning, one comput
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Logical Treatment of Incomplete/Uncertain Information Relying on Different Systems of Rough Setsground knowledge and we cannot say more about an arbitrary set or about its members than its lower and upper approximations make possible. New membership relations have to be introduced. The semantics of classical logic is based on classical set theory. An important question: Is it possible to build
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P-T Probability Framework and Semantic Information G Theory Tested by Seven Difficult Taskson theory. This theory uses the P-T probability framework so that likelihood functions and truth functions (or membership functions), as well as sampling distributions, can be put into the semantic mutual information formula at the same time. Hence, we can connect statistics and (fuzzy) logic. Rate-
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Trilevel Multi-criteria Decision Analysis Based on Three-Way DecisionCDA). The main motivations and contributions are a new understanding of multi-criteria decision analysis at three levels: 1) single-criterion-level analysis with respect to individual criteria, 2) multi-criteria-level aggregation by pooling results from many criteria, and 3) multi-methods-level ense
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