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Titlebook: Data Science; 10th International C Chengzhong Xu,Haiwei Pan,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The Au

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樓主: Gram114
21#
發(fā)表于 2025-3-25 05:12:28 | 只看該作者
Conference proceedings 2024and engine; data security and privacy; big data mining and knowledge management...Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis..
22#
發(fā)表于 2025-3-25 10:10:29 | 只看該作者
23#
發(fā)表于 2025-3-25 12:03:50 | 只看該作者
Die Dreiermenge von Georg Cantor,ing, and then tested in a test set. Experimental results with relatively few model parameters show that our proposed method has good detection performance, while the model requires less storage space and has low computational overhead, making it suitable for network traffic detection and classification under edge networks.
24#
發(fā)表于 2025-3-25 19:28:26 | 只看該作者
https://doi.org/10.1007/978-3-642-52575-9and using the adaptive mechanism to fuse these features, we aim to improve the accuracy of network performance evaluation. Furthermore, our extensive experiments have shown that TrafficNet can improve the Mean Squared Error(MSE) by 58.3% compared with the SOTA models.
25#
發(fā)表于 2025-3-25 22:14:29 | 只看該作者
https://doi.org/10.1007/978-3-322-93533-5n addition, we put forward the Target Category Learner (TCL) module to simulate human questioning thinking, and apply a penalty mechanism to reduce repetition. Experimental results on the GuessWhat?! dataset show QIRE’s competitiveness in question quality and dialog effectiveness compared to existing methods.
26#
發(fā)表于 2025-3-26 02:27:31 | 只看該作者
https://doi.org/10.1007/978-3-642-90899-6formation in videos, resulting in more precise prediction and analysis. The experimental results show that our multimodal variable-channel spatial-temporal semantic action recognition network achieves 98.3% and 89.9% accuracy in classifying actions on the large-scale human activity datasets NTU-RGB+D 60 and NTU-RGB+D 120 respectively.
27#
發(fā)表于 2025-3-26 08:03:51 | 只看該作者
28#
發(fā)表于 2025-3-26 12:19:14 | 只看該作者
A Lightweight Edge Network Intrusion Detection System Based on MobileViting, and then tested in a test set. Experimental results with relatively few model parameters show that our proposed method has good detection performance, while the model requires less storage space and has low computational overhead, making it suitable for network traffic detection and classification under edge networks.
29#
發(fā)表于 2025-3-26 15:05:23 | 只看該作者
30#
發(fā)表于 2025-3-26 18:59:27 | 只看該作者
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