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Titlebook: Big Data; 8th CCF Conference, Hong Mei,Weiguo Zhang,Li Wang Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 artificial

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樓主: 忠誠
21#
發(fā)表于 2025-3-25 03:50:10 | 只看該作者
,Multi Dimensional Evaluation of Middle School Students’ Physical and Mental Quality and Intelligentollaborative filtering), using Embedding technology and graph convolutional neural network to mine the attributes and interactive relationship features in the data, and then through the fusion of feature vector expressions to achieve personalized exercise program recommendations. The design and impl
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發(fā)表于 2025-3-25 07:50:39 | 只看該作者
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發(fā)表于 2025-3-25 11:39:18 | 只看該作者
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發(fā)表于 2025-3-25 19:48:17 | 只看該作者
Image Compressed Sensing Using Neural Architecture Search,struction algorithms in both running speed and reconstruction quality. However, it is a time-consuming procedure even for an expert to efficiently design a high-performance network for image CS because of various combination of different kernel size and filter number in each layer. In this paper, a
25#
發(fā)表于 2025-3-25 20:05:33 | 只看該作者
26#
發(fā)表于 2025-3-26 03:20:46 | 只看該作者
Rotation-DPeak: Improving Density Peaks Selection for Imbalanced Data, and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overco
27#
發(fā)表于 2025-3-26 05:31:49 | 只看該作者
28#
發(fā)表于 2025-3-26 08:49:45 | 只看該作者
Improving Small-Scale Dataset Classification Performance Through Weak-Label Samples Generated by Insamples, the amount of available training data is always limited (real data). Generative Adversarial Network (GAN) has good performance in generating artificial samples (generated data), the generated samples can be used as supplementary data to make up for the problem of small dataset with small sa
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發(fā)表于 2025-3-26 16:20:21 | 只看該作者
30#
發(fā)表于 2025-3-26 18:37:08 | 只看該作者
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