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Titlebook: Intelligent Information Processing XI; 12th IFIP TC 12 Inte Zhongzhi Shi,Jean-Daniel Zucker,Bo An Conference proceedings 2022 IFIP Internat

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發(fā)表于 2025-3-21 19:01:22 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Intelligent Information Processing XI
副標(biāo)題12th IFIP TC 12 Inte
編輯Zhongzhi Shi,Jean-Daniel Zucker,Bo An
視頻videohttp://file.papertrans.cn/470/469715/469715.mp4
叢書名稱IFIP Advances in Information and Communication Technology
圖書封面Titlebook: Intelligent Information Processing XI; 12th IFIP TC 12 Inte Zhongzhi Shi,Jean-Daniel Zucker,Bo An Conference proceedings 2022 IFIP Internat
描述.This book constitutes the refereed proceedings of the 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, held in Qingdao, China, in July 2022...?..The 37 full papers and 6 short papers presented were carefully reviewed and selected from 57 submissions. They are organized in topical sections on Machine Learning, Data Mining, Multiagent Systems, Social Computing, Blockchain Technology, Game Theory and Emotion, Pattern Recognition, Image Processing and Applications..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computer networks; computer systems; computer vision; correlation analysis; data
版次1
doihttps://doi.org/10.1007/978-3-031-03948-5
isbn_softcover978-3-031-05912-4
isbn_ebook978-3-031-03948-5Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2022
The information of publication is updating

書目名稱Intelligent Information Processing XI影響因子(影響力)




書目名稱Intelligent Information Processing XI影響因子(影響力)學(xué)科排名




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Intelligent Information Processing XI978-3-031-03948-5Series ISSN 1868-4238 Series E-ISSN 1868-422X
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IFIP Advances in Information and Communication Technologyhttp://image.papertrans.cn/i/image/469715.jpg
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A Novel Feature Selection Algorithm Based on?Aquila Optimizer for?COVID-19 Classificationmage sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively.
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