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

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31#
發(fā)表于 2025-3-26 23:30:24 | 只看該作者
32#
發(fā)表于 2025-3-27 02:48:25 | 只看該作者
0302-9743 4882 - 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 th
33#
發(fā)表于 2025-3-27 06:09:03 | 只看該作者
https://doi.org/10.1007/978-3-662-00854-6lass domain distribution matching loss, is proposed to better align the features of different domains in the high-dimensional space. Experiments are conducted on three benchmark datasets to compare our model with other mainstream models, and the results achieve higher accuracy.
34#
發(fā)表于 2025-3-27 11:13:19 | 只看該作者
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發(fā)表于 2025-3-27 15:47:30 | 只看該作者
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發(fā)表于 2025-3-27 19:01:58 | 只看該作者
Multi-mode Graph Attention-Based Anomaly Detection on Attributed Networks detection methods encounter challenges in following aspects: capturing sparsity, nonlinearity, and ensuring the uniqueness of anomalies. To address these issues, this paper introduces an autoencoder framework built upon multi-mode graph attention networks, which models attribute networks using grap
37#
發(fā)表于 2025-3-27 22:12:24 | 只看該作者
A Hierarchical Multi-scale Cortical Learning Algorithm for Time Series Forecastingct temporal dependencies of time series, it ignores the characteristics of the data and can’t deal with the intricate temporal patterns within the sequence. Multi-scale information is crucial for modeling time series, but is not fully studied in the CLA. To this end, we propose a Hierarchical Multi-
38#
發(fā)表于 2025-3-28 05:44:43 | 只看該作者
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發(fā)表于 2025-3-28 07:22:46 | 只看該作者
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發(fā)表于 2025-3-28 10:37:23 | 只看該作者
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