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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Zhanjun Si,Wei Chen Conference proceedings

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发表于 2025-3-21 18:45:16 | 显示全部楼层 |阅读模式
期刊全称Advanced Intelligent Computing Technology and Applications
期刊简称20th International C
影响因子2023De-Shuang Huang,Zhanjun Si,Wei Chen
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Zhanjun Si,Wei Chen Conference proceedings
影响因子.This 6-volume set LNAI 14875-14880 constitutes - in conjunction with the 13-volume set LNCS 14862-14874 and the 2-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024...The total of 863 regular papers were carefully reviewed and selected from 2189 submissions...The intelligent computing annual conference primarily aims to promote research, development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of disciplines. This conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them...The intelligent computing technology includes a range of techniques such as Artificial Intelligence, Pattern Recognition, Evolutionary Computing, Informatics Theories and Applications, Computational Neuroscience & Bioscience, Soft Computing, Human Computer Interface Issues, etc..
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/167163.jpg
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978-981-97-5677-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Versicherungsformen der Praxis,ing external knowledge from knowledge bases (KBs), such as extracting entity concepts and evidence within articles, significantly enhances rumor detection performance. However, two limitations still exist: 1)When extracting entity concepts from KBs, entity ambiguity may introduce inappropriate conce
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,Regelungstechnische Verhältnisse,ed, traditional detection methods hinder effective disease management. We introduce USST, a novel cassava leaf disease detection approach to address this challenge. USST employs a synergistic combination of two attention mechanisms for enhanced feature learning: optimized self-guided attention (SGA)
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