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Titlebook: Big Data; 11th CCF Conference, Enhong Chen,Yang Gao,Wanqi Yang Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s),

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樓主: 討論小組
41#
發(fā)表于 2025-3-28 16:09:55 | 只看該作者
https://doi.org/10.1057/9781137491121iew Graph Transformer module, and a multi-view attention module, which can explore the complementarity, consistency, and semantic relevance of multiple different views in online social networks. Experimental results show that MV-GT outperforms many existing methods and also demonstrates the effectiv
42#
發(fā)表于 2025-3-28 19:53:22 | 只看該作者
43#
發(fā)表于 2025-3-29 00:44:39 | 只看該作者
Female Genital Mutilation/-Cutting the relative temporal distance between power consumption data points and their neighbors. We validate the proposed model using the SGCC dataset, and our experimental results demonstrate high accuracy, precision, F1-score, and AUC values.
44#
發(fā)表于 2025-3-29 03:25:04 | 只看該作者
45#
發(fā)表于 2025-3-29 09:06:49 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsonon their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramaticall
46#
發(fā)表于 2025-3-29 11:33:00 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsontiple individual models have demonstrated promising results for forecasting performance. However, these models also face the issues of high computational cost and time consumption when dealing with multiple time-series. To address these issues, this paper proposes a novel framework that integrates m
47#
發(fā)表于 2025-3-29 16:12:52 | 只看該作者
48#
發(fā)表于 2025-3-29 20:36:09 | 只看該作者
49#
發(fā)表于 2025-3-30 01:45:13 | 只看該作者
Sara K. Howe,Antonnet Renae Johnsonnly focus on extracting information from high-level features, while ignoring the influence of low-level features on FGVC. Based on this, this paper integrates low-level detailed information and high-level semantic information to improve the model performance by enhancing the feature representation a
50#
發(fā)表于 2025-3-30 05:49:01 | 只看該作者
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