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Titlebook: Intelligent Computing Theories and Application; 18th International C De-Shuang Huang,Kang-Hyun Jo,Abir Hussain Conference proceedings 2022

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书目名称Intelligent Computing Theories and Application
副标题18th International C
编辑De-Shuang Huang,Kang-Hyun Jo,Abir Hussain
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Theories and Application; 18th International C De-Shuang Huang,Kang-Hyun Jo,Abir Hussain Conference proceedings 2022
描述This two-volume set of LNCS 13393 and LNCS 13394 constitutes - in conjunction with the volume LNAI 13395 - the refereed proceedings of the 18th International Conference on Intelligent Computing, ICIC 2022, held in Xi‘an, China, in August 2022. The 209 full papers of the three proceedings volumes were carefully reviewed and selected from 449 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 focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology..
出版日期Conference proceedings 2022
关键词artificial intelligence; classification; computer networks; computer science; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-031-13829-4
isbn_softcover978-3-031-13828-7
isbn_ebook978-3-031-13829-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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978-3-031-13828-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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A Comparison Study of Predicting lncRNA-Protein Interactions via Representative Network Embedding Mee-art network embedding methods are evaluated on five benchmark datasets containing three ones in human and two in plants. Experimental results demonstrate that recent network embedding methods, e.g., metapath2vec, achieve better prediction performances. The data and code in this study are available
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GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph de attributes, a graph attention network was used to construct feature vectors in subgraphs to extract deep embedded features. Finally, a neural network was applied to make predictions about potential associations. The experimental results showed that the GATSDCD model outperforms existing methods i
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Real-Time Optimal Scheduling of Large-Scale Electric Vehicles Based on Non-cooperative Gameging strategy to minimize the charging cost during the game. The charging and discharging strategy of electric vehicle population reaches a stable Nash equilibrium, and the optimization goal can be got. The algorithm proposed in this paper can also reduce the total imported electricity and comprehen
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TBC-Unet: U-net with Three-Branch Convolution for Gliomas MRI Segmentationel category imbalance. We construct a new loss function to address the problem of category imbalance. Extensive experiments on BraTS datasets demonstrate that the proposed method achieves very competitive results with the state-of-the-art approaches.
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