C
Chengye Li
Researcher at First Affiliated Hospital of Wenzhou Medical University
Publications - 31
Citations - 2391
Chengye Li is an academic researcher from First Affiliated Hospital of Wenzhou Medical University. The author has contributed to research in topics: Local optimum & Support vector machine. The author has an hindex of 20, co-authored 28 publications receiving 1356 citations. Previous affiliations of Chengye Li include Wenzhou Medical College.
Papers
More filters
Journal ArticleDOI
Boosted binary Harris hawks optimizer and feature selection
TL;DR: A novel HHO called IHHO is proposed by embedding the salp swarm algorithm (SSA) into the original HHO to improve the search ability of the optimizer and expand the application fields and the experimental results reveal that the proposed I HHO has better accuracy rates over other compared wrapper FS methods.
Journal ArticleDOI
An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks
Yueting Xu,Huiling Chen,Ali Asghar Heidari,Ali Asghar Heidari,Jie Luo,Qian Zhang,Xuehua Zhao,Chengye Li +7 more
TL;DR: The proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction and demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance.
Journal ArticleDOI
Developing a new intelligent system for the diagnosis of tuberculous pleural effusion
Chengye Li,Lingxian Hou,Bishundat Yanesh Sharma,Huaizhong Li,ChengShui Chen,Yuping Li,Xuehua Zhao,Hui Huang,Zhennao Cai,Huiling Chen +9 more
TL;DR: The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples and can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies.
Journal ArticleDOI
Gaussian mutational chaotic fruit fly-built optimization and feature selection
Xiang Zhang,Yueting Xu,Caiyang Yu,Ali Asghar Heidari,Ali Asghar Heidari,Shimin Li,Huiling Chen,Chengye Li +7 more
TL;DR: Numerical results show that two embedded strategies will effectively boost the performance of FOA for optimization tasks and prove that MCFOA can obtain the optimal classification accuracy.
Journal ArticleDOI
Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy
Zhennao Cai,Jianhua Gu,Jie Luo,Qian Zhang,Huiling Chen,Zhifang Pan,Zhifang Pan,Yuping Li,Chengye Li +8 more
TL;DR: A new parameter learning strategy based on an improved grey wolf optimization (IGWO) strategy, in which a new hierarchical mechanism was established to improve the stochastic behavior, and exploration capability of grey wolves, is proposed.