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A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd.
Tang, Jianheng; Fan, Kejia; Xie, Wenxuan; Zeng, Luomin; Han, Feijiang; Huang, Guosheng; Wang, Tian; Liu, Anfeng; Zhang, Shaobo.
Afiliação
  • Tang J; School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Fan K; School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Xie W; School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Zeng L; School of Civil Engineering, Central South University, Changsha, 410083, China.
  • Han F; School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Huang G; School of computer Science and Engineering, Hunan First Normal University, Changsha, 410205, China.
  • Wang T; Department of Artificial Intelligence and Future Networks, Beijing Normal University & UIC, Zhuhai, Guangdong, China.
  • Liu A; School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
  • Zhang S; School of Computer Science and Engineering of the Hunan University of Science and Technology, Xiangtan, 411201, China.
Comput Commun ; 206: 85-100, 2023 Jun 01.
Article em En | MEDLINE | ID: mdl-37197296

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article