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Titlebook: Data Science; 4th International Co Qinglei Zhou,Qiguang Miao,Zeguang Lu Conference proceedings 2018 Springer Nature Singapore Pte Ltd. 2018

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樓主: Racket
21#
發(fā)表于 2025-3-25 07:22:36 | 只看該作者
Edwin R. Dubose Jr.,Earl E. Shelping between the pending paper content and the experts’ research interests, and then recommend experts based on the similarity. Experimental results show that the proposed diversified knowledge model can find the appropriate paper reviewers effectively, which can reduce the workload of the editors greatly.
22#
發(fā)表于 2025-3-25 08:40:06 | 只看該作者
Peter J. Rich,Matthew B. Langtoncross space real-time interaction with remote operation. This technology can provide a simple optimal solution for China’s medical conjoined and grading treatment system with a high efficient and low cost [.].
23#
發(fā)表于 2025-3-25 14:44:26 | 只看該作者
Steve Bennett,Trevor Barker,Mariana Lilleynd concave features than CX values with solvent accessibility, solvent accessibility, and B-factor’s Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm.
24#
發(fā)表于 2025-3-25 17:24:55 | 只看該作者
25#
發(fā)表于 2025-3-25 23:19:31 | 只看該作者
Auxiliary Disease and Treatment System of Aortic Disease Based on Mixed Reality,cross space real-time interaction with remote operation. This technology can provide a simple optimal solution for China’s medical conjoined and grading treatment system with a high efficient and low cost [.].
26#
發(fā)表于 2025-3-26 02:50:27 | 只看該作者
27#
發(fā)表于 2025-3-26 05:58:42 | 只看該作者
28#
發(fā)表于 2025-3-26 12:28:36 | 只看該作者
Jim Allen,Yuki Inenaga,Keiichi Yoshimotogainst some normal methods, such as methods based emotion lexicon, machine learning methods, LSTM and other neural network models. Experiments show that these two proposed models have achieved better results in text sentiment analysis. The best model CNN-BLSTM is better than the normal neural network models in accuracy.
29#
發(fā)表于 2025-3-26 16:16:12 | 只看該作者
30#
發(fā)表于 2025-3-26 18:40:47 | 只看該作者
https://doi.org/10.1007/978-94-007-5386-0his paper, we give a general overview of knowledge graph’s construction research based on knowledge embedding, including knowledge representation, knowledge embedding and so on. Then we summarize the challenge for the knowledge graph and the future development trend.
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