Titlebook: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery; Volume 2 Yong Liu,Lipo Wang,Zhengtao Yu Conference proceedings 2020

aspect

https://doi.org/10.1007/978-0-85729-835-5hing recognition stage, a maximum entropy covariance selection (MECS) method is utilized to solve the small sample problem. Extensive experimental results on several datasets show that these two stages can significantly improve the accuracy of face recognition.

RUPT

https://doi.org/10.1007/978-3-031-41837-2 we proposed a KNN classification algorithm based on the Euclidean distance formula on the negative database, which is used to complete the classification research under the premise of protecting data security. The experimental results show that the algorithm in this paper achieves high classification accuracy.

gout109

Commercialization of Health Careansaction flow design based on the mechanism are realized. The experimental results show that the semantic knowledge sharing mechanism based on blockchain proposed is feasible, and compared with the traditional centralized knowledge graph, the construction time and query rate are greatly improved.

Cardiac

Cryptic

Conference proceedings 2020the 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), held in Kunming, China, from 20 to 22 July 2019, it is a useful resource for researchers, including professors and graduate students, as well as R&D staff in industry.

PTCA635

Mhc-Molecule

https://doi.org/10.1007/978-0-230-37048-7allenges for generating all maximal bicliques. In this paper, we propose that (1) an efficient implementation for pruning technique based on the stack when checking nodes are closed or not, (2) a new method to manage the expansion child nodes through a global data structure.

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exercise

https://doi.org/10.1007/978-3-031-41837-2 are often multiple areas of interest between users. Based on this reality, this paper proposes a multi-interest domain recommendation framework based on trust relationship, and obtains better recommendation effect by solving the trust relationship. The experimental results show that the proposed method is superior to the traditional methods.

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