Titlebook: Advanced Intelligent Computing Technology and Applications; 19th International C De-Shuang Huang,Prashan Premaratne,Abir Hussain Conference

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https://doi.org/10.1057/9780230306790rmation lost due to the pooling layer of the CNNs, and a decoder is responsible for fusing the feature information extracted from the two stages. Extensive experiments demonstrate that DAF can improve the performance of CapsNets on complex datasets and reduce the number of parameters, GPU memory cos

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臨時(shí)抱佛腳

https://doi.org/10.1007/978-4-431-54559-0CN-LP method is comparable to the meta-heuristic algorithms in the OSSP benchmark instances, but the solution quality and solution efficiency of the GCN-LP method are significantly better than the meta-heuristic algorithms in the large-scale OSSP random instances. Compared with the other graph neura

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Tridib Banerjee,William C. Baer1DCG, A-BiGRU, and ST-1DCG. The performance of the MT-1DCG model is validated through multiple experiments, demonstrating superior results compared to A-BiGRU and ST-1DCG models. Standard evaluation metrics, including accuracy, sensitivity, specificity, and ROC, are employed to assess model performa

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Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models978-1-4302-3865-2

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Cross-Scale Dynamic Alignment Network for Reference-Based Super-Resolution978-1-4302-0042-0