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Titlebook: Computational Intelligence; Jonathan Garibaldi,Christian Wagner,Kevin Warwick Conference proceedings 2023 The Editor(s) (if applicable) an

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發(fā)表于 2025-3-21 16:38:55 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computational Intelligence
編輯Jonathan Garibaldi,Christian Wagner,Kevin Warwick
視頻videohttp://file.papertrans.cn/233/232364/232364.mp4
概述Highlights recent research on Computational Intelligence.Gathers revised papers presented at the 12th and 13th International Joint Conference on Computational Intelligence.Written by leading experts i
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Computational Intelligence;  Jonathan Garibaldi,Christian Wagner,Kevin Warwick Conference proceedings 2023 The Editor(s) (if applicable) an
描述.This book includes a set of selected revised and extended versions of the best papers presented at the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) – held as an online event, from October 25 to 27, 2021. We focus on three outstanding fields of Computational Intelligence through the selected panel, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and on the other hand to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current field of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represent a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques in various fieldsof applications..
出版日期Conference proceedings 2023
關(guān)鍵詞Computational Intelligence; Evolutionary Computation; Fuzzy Computation; Neural Computation; IJCCI Paper
版次1
doihttps://doi.org/10.1007/978-3-031-46221-4
isbn_softcover978-3-031-47554-2
isbn_ebook978-3-031-46221-4Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書(shū)目名稱(chēng)Computational Intelligence影響因子(影響力)




書(shū)目名稱(chēng)Computational Intelligence影響因子(影響力)學(xué)科排名




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,Risk Assessment Modeling Based on?a?Graded Fuzzy Concept Lattice,aper self-contained, we provide all the necessary information concerning fuzzy concept lattices and their graded version. Theoretical models are further considered in the analysis of pandemic scenario which provides an insight into practical modeling of risk management processes and assessment of particular risks.
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,Evaluation of?Gated Recurrent Neural Networks for?Embedded Systems Applications,t cell (STAR). We explain the algorithmic details and how these cells can be combined and integrated to build complex structures. We illustrate our approach with experimental results using different architectures and implementations on dedicated test cases.
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1860-949X e on Computational Intelligence.Written by leading experts i.This book includes a set of selected revised and extended versions of the best papers presented at the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) – held as an online event, from October 25 to 27, 2021. W
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Conference proceedings 2023tional Intelligence (IJCCI 2021) – held as an online event, from October 25 to 27, 2021. We focus on three outstanding fields of Computational Intelligence through the selected panel, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. Besides presenting the recent advances
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A route to stochastic mechanics,he Autonomous Driving System for a specific type of boat has been updated and it is more detailed here. Furthermore, the ad-hoc Simulator developed to test and validate the ADS was improved in realism degree and other simulations have been performed. Outcoming data and post-analyses are reported here together with our conclusions.
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https://doi.org/10.1007/978-3-642-82322-0o see that the approach using capsule architecture achieves strong results and exceeds other techniques on a wide range of datasets from industrial application to face recognition benchmarks while being easy to use and optimise.
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