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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Jiayang Guo Conference proce

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发表于 2025-3-21 17:04:39 | 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,Xiankun Zhang,Jiayang Guo
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,Xiankun Zhang,Jiayang Guo Conference proce
影响因子.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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书目名称Advanced Intelligent Computing Technology and Applications被引频次学科排名




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Anomaly Detection of Transmission Line Large Metal Based on EGFPN-YOLO and UAVsYOLO is proposed. The model combines the latest YOLOv8n algorithm, with particular improvements in the detection head, designing an efficient and lightweight detection head called EfficientHead. Additionally, inspired by the RepGFPN method, the Neck part of the model is improved by introducing the C
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AYOLOv8: Improved Detector Based on YOLOv8 to Focus More on Small and Medium Objectseries and has attracted the attention of scholars due to its excellent detection performance. However, this model shows slightly inferior detection performance for small and medium-sized objects. To cope with this issue, in this paper, we propose three effective modules and embed them to YOLOv8 for
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SFERNet: Student Facial Expression Recognition Using Superpixel-Assisted Global Semantic Enhancementapturing non-local dependencies with semantic continuity and representing fine-grained local features. To tackle these issues, we propose SFERNet, a novel emotion computation network based on semantic-enhanced global dependency modeling and fine-grained feature compensation. Firstly, we introduce a
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