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Titlebook: Intelligence Science and Big Data Engineering; 8th International Co Yuxin Peng,Kai Yu,Xingpeng Jiang Conference proceedings 2018 Springer N

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樓主: 萬能
21#
發(fā)表于 2025-3-25 03:29:23 | 只看該作者
Efficiency Ranking via Combining DEA Evaluation and Bayesian Prediction for Logistics Enterpriseshui, China. Empirical results show that the DEA-Bayes approach has good discrimination for efficiency ranking. Unlike expert scoring, our evaluation process is based on logistics enterprise data and easy to operate.
22#
發(fā)表于 2025-3-25 10:35:16 | 只看該作者
23#
發(fā)表于 2025-3-25 15:18:54 | 只看該作者
Kun Wang,Songsong Wu,Yufeng Qiu,Fei Wu,Xiaoyuan Jing
24#
發(fā)表于 2025-3-25 18:45:56 | 只看該作者
A Service Agents Division Method Based on Semantic Negotiation of Concepts of service agent, and divided the agents which have similar functions and understanding ability into the same services community according to the result of negotiation. Finally, an experimental comparison between our method and other methods is presented, the results show that the semantic negotiation method has the least error rate.
25#
發(fā)表于 2025-3-25 20:25:33 | 只看該作者
Learning Semantic Double-Autoencoder with Attribute Constraint for Zero-Shot Recognitioncoder paradigm to learn a projection function in the source and target domains simultaneously. In addition, we introduce one constraint on source domain attributes into this work to improve the performance of our model. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method.
26#
發(fā)表于 2025-3-26 03:45:44 | 只看該作者
Attribute Value Matching with Limited Budgetstic strategy which prefers to resolve those . equivalents such that maximal benefit to data consistency can be achieved with the limited budget. Experimental evaluations show the effectiveness of our approach.
27#
發(fā)表于 2025-3-26 04:41:05 | 只看該作者
Moth-Flame Optimization Algorithm Based on Adaptive Weight and Simulated Annealingsolutions with a certain probability, which can further alleviate the problem that MFO is easy to fall into local optimum and will also enhance the global search ability of MFO algorithm. The experimental results show that the improved algorithm is superior to other optimization algorithms in the convergence precision and the stability.
28#
發(fā)表于 2025-3-26 11:06:24 | 只看該作者
29#
發(fā)表于 2025-3-26 13:25:47 | 只看該作者
30#
發(fā)表于 2025-3-26 17:58:45 | 只看該作者
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