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Titlebook: Intelligent Computing Theories and Application; 17th International C De-Shuang Huang,Kang-Hyun Jo,Vitoantonio Bevilacqu Conference proceedi

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发表于 2025-3-21 18:03:27 | 显示全部楼层 |阅读模式
书目名称Intelligent Computing Theories and Application
副标题17th International C
编辑De-Shuang Huang,Kang-Hyun Jo,Vitoantonio Bevilacqu
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Theories and Application; 17th International C De-Shuang Huang,Kang-Hyun Jo,Vitoantonio Bevilacqu Conference proceedi
描述This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021. The 192 full papers of the three proceedings volumes were carefully reviewed and selected from 458 submissions...The ICIC theme unifies 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. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” .The papers are organized in the following subsections: Evolutionary Computation and Learning, Image and signal Processing, Information Security, Neural Networks, Pattern Recognition Swarm Intelligence and Optimization, and Virtual Reality and Human-Computer Interaction..
出版日期Conference proceedings 2021
关键词artificial intelligence; computer networks; computer systems; computer vision; correlation analysis; data
版次1
doihttps://doi.org/10.1007/978-3-030-84522-3
isbn_softcover978-3-030-84521-6
isbn_ebook978-3-030-84522-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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An Efficient Competitive Swarm Optimizer for Solving Large-Scale Multi-objective Optimization Proble strong exploration ability, denoted as E-CSO, is presented in this paper, which designs a novel three-particle-based particle updating strategy to improve the search efficiency. The experimental results validate the high efficiency and effectiveness of our proposed approach when solving various LSMOPs.
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Hyperspectral Image Classification with Locally Linear Embedding, 2D Spatial Filtering, and SVMh spatial information and spectral information are taken into consideration for HSI classification. Experiments demonstrate that this new algorithm is extremely competitive when compared to several existing methods.
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An Improved Teaching-Learning-Based Optimization for Multitask Optimization Problemsed into the teaching stage which allow students to receive a wide range of knowledge and find individuals with great differences. MTTLBO is compared with some advanced evolutionary multitask algorithms. The experimental results state clearly that the MTTLBO algorithm owns excellent performance on nine sets of single-objective problems.
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