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Titlebook: Web and Big Data; 6th International Jo Bohan Li,Lin Yue,Toshiyuki Amagasa Conference proceedings 2023 The Editor(s) (if applicable) and The

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樓主: Corrugate
11#
發(fā)表于 2025-3-23 10:29:35 | 只看該作者
The Influence of the Student’s Online Learning Behaviors on the Learning Performanceorrelated with the basic question factors and comprehensive question factors. Therefore, teachers and students who use 5y platform should focus on the use of knowledge point tests and unit tests to improve the quality of teaching and learning within the limited class time.
12#
發(fā)表于 2025-3-23 15:37:41 | 只看該作者
The Influence of the Student’s Online Learning Behaviors on the Learning Performanceorrelated with the basic question factors and comprehensive question factors. Therefore, teachers and students who use 5y platform should focus on the use of knowledge point tests and unit tests to improve the quality of teaching and learning within the limited class time.
13#
發(fā)表于 2025-3-23 20:16:09 | 只看該作者
14#
發(fā)表于 2025-3-23 23:11:15 | 只看該作者
15#
發(fā)表于 2025-3-24 03:31:16 | 只看該作者
16#
發(fā)表于 2025-3-24 08:24:29 | 只看該作者
17#
發(fā)表于 2025-3-24 11:20:08 | 只看該作者
Specific Emitter Identification Based on ACO-XGBoost Feature Selectionclassified by the proposed model. Comparing with Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and XGBoost feature selection methods, the identification performance of the proposed model is effectively improved.
18#
發(fā)表于 2025-3-24 15:45:08 | 只看該作者
19#
發(fā)表于 2025-3-24 22:46:12 | 只看該作者
NED-GNN: Detecting and?Dropping Noisy Edges in?Graph Neural Networksting the weights between sampled negative edges and existing edges for each node, NED-GNN detects and removes noisy edges. Extensive experiments are conducted on benchmark datasets and the promising performance compared with baseline methods indicates the effectiveness of our model.
20#
發(fā)表于 2025-3-24 23:36:05 | 只看該作者
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