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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Wei Chen,Qinhu Zhang Conference proceeding

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期刊全稱Advanced Intelligent Computing Technology and Applications
期刊簡(jiǎn)稱20th International C
影響因子2023De-Shuang Huang,Wei Chen,Qinhu Zhang
視頻videohttp://file.papertrans.cn/168/167158/167158.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Wei Chen,Qinhu Zhang Conference proceeding
影響因子.This 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-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...This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology...?..?.
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書(shū)目名稱Advanced Intelligent Computing Technology and Applications影響因子(影響力)




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書(shū)目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications被引頻次




書(shū)目名稱Advanced Intelligent Computing Technology and Applications被引頻次學(xué)科排名




書(shū)目名稱Advanced Intelligent Computing Technology and Applications年度引用




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HRMNN: Heterogeneous Relationship Mined Graph Neural Networkerate richer node representation. Experiments such as node classification and link prediction on multiple datasets demonstrate that HRMNN has better performance compared to state-of-the-art baselines.
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Knowledge Completion Method Based on Relational Embedding with GNNpaper analyzes some models such as CompGCN and SE-GNN. They have shown excellent performance on several datasets. There is also an analysis of the future development of GNN-based methods for knowledge completion.
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