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Titlebook: Data Science; 8th International Co Yang Wang,Guobin Zhu,Zeguang Lu Conference proceedings 2022 The Editor(s) (if applicable) and The Author

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31#
發(fā)表于 2025-3-27 00:47:23 | 只看該作者
1865-0929 ientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in? August, 2022...The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Mining and Knowledge M
32#
發(fā)表于 2025-3-27 04:48:48 | 只看該作者
33#
發(fā)表于 2025-3-27 06:35:30 | 只看該作者
34#
發(fā)表于 2025-3-27 12:44:11 | 只看該作者
35#
發(fā)表于 2025-3-27 15:38:23 | 只看該作者
Managing Brands in Competitive Marketplaces multiple relationships. The MrAPR model better describes the characteristics of user interest and can be compatible with the existing sequence recommendation methods. The experimental results on two real-world datasets clearly show the effectiveness of the MrAPR model.
36#
發(fā)表于 2025-3-27 19:05:25 | 只看該作者
Interpreting the Chemical Residues StoryR images by shrinking the network, which will share similar degradation with real-world images. Finally, we make paired data of the generated real LR images and HR images for training the SR network. Our approach can obtain better results than the recent SR approach on the NTIRE2020 real-world SR challenge Track1 dataset.
37#
發(fā)表于 2025-3-28 00:55:01 | 只看該作者
38#
發(fā)表于 2025-3-28 05:39:45 | 只看該作者
Automatic Generation of Graduation Thesis Comments Based on Multilevel Analysis review work of the graduation thesis from pure manual operation to machine review combined with manual operation can not only reduce manpower consumption but also make the review work more objective and fair, making it more objective on the basis of traditional subjective review.
39#
發(fā)表于 2025-3-28 06:25:22 | 只看該作者
Multirelationship Aware Personalized Recommendation Model multiple relationships. The MrAPR model better describes the characteristics of user interest and can be compatible with the existing sequence recommendation methods. The experimental results on two real-world datasets clearly show the effectiveness of the MrAPR model.
40#
發(fā)表于 2025-3-28 11:24:19 | 只看該作者
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