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Titlebook: Knowledge Science, Engineering and Management; 17th International C Cungeng Cao,Huajun Chen,Yonghao Wang Conference proceedings 2024 The Ed

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31#
發(fā)表于 2025-3-26 22:05:55 | 只看該作者
32#
發(fā)表于 2025-3-27 01:19:25 | 只看該作者
33#
發(fā)表于 2025-3-27 08:17:57 | 只看該作者
34#
發(fā)表于 2025-3-27 09:52:18 | 只看該作者
35#
發(fā)表于 2025-3-27 15:35:33 | 只看該作者
36#
發(fā)表于 2025-3-27 19:38:13 | 只看該作者
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發(fā)表于 2025-3-27 22:30:15 | 只看該作者
GA-MEPS: Multiple Experts Portfolio Selection Based on?Genetic Algorithmowever, existing methods often overlook two essential factors: expert diversity and adaptive expert selection, both of which significantly impact portfolio returns. To address these issues, this paper proposes a novel multiple experts portfolio selection method based on genetic algorithm (GA-MEPS).
38#
發(fā)表于 2025-3-28 05:30:43 | 只看該作者
Deep Learning and?Machine Learning-Based Approaches to?Inferring Social Media Network Users’ Interesdresses the challenge posed by inactive users who rarely share or interact on social media, making it difficult to assess their profiles due to insufficient information. To infer and forecast the interests of these inactive users, the study examines how individuals’ interests can be deduced from the
39#
發(fā)表于 2025-3-28 07:02:00 | 只看該作者
Customer Segmentation for?Telecommunication Using Machine Learningrgeted strategies that meet their customers’ needs, increase customer satisfaction, and drive revenue growth. Maintaining a competitive position will require diversifying business models in the data plan area. While previous studies have focused on clustering and prediction to identify customer chur
40#
發(fā)表于 2025-3-28 11:03:34 | 只看該作者
DP-MFRNN: Difficulty Prediction for Examination Questions Based on Neural Network Frameworkw accuracy, and substantial workload. This approach significantly hampers the progress and advancement of intelligent education evaluation systems. In order to address these challenges, we propose DP-MFRNN, a bidirectional recurrent neural network model based on multi-feature attention. The DP-MFRNN
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