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Cited by in F6Publishing
For: He W, Xie Y, Lu H, Wang M, Chen H. Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm. Symmetry 2020;12:1651. [DOI: 10.3390/sym12101651] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
Number Citing Articles
1 Cheng X, Zhu L, Lu H, Wei J, Wu N, Shao H. Robot Path Planning Based on an Improved Salp Swarm Algorithm. Journal of Sensors 2022;2022:1-16. [DOI: 10.1155/2022/2559955] [Reference Citation Analysis]
2 Li J, Ren H, Li C, Chen H. A novel and efficient salp swarm algorithm for large-scale QoS-aware service composition selection. Computing. [DOI: 10.1007/s00607-022-01080-7] [Reference Citation Analysis]
3 Chen Z, Yang M, Wen Y, Jiang S, Liu W, Huang H. Prediction of atherosclerosis using machine learning based on operations research. Math Biosci Eng 2022;19:4892-910. [PMID: 35430846 DOI: 10.3934/mbe.2022229] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
4 Patro SP, Padhy N, Sah RD. Anticipation of Heart Disease Using Improved Optimization Techniques. Communications in Computer and Information Science 2022. [DOI: 10.1007/978-3-031-21750-0_8] [Reference Citation Analysis]
5 Ren H, Li J, Chen H, Li C. Stability of salp swarm algorithm with random replacement and double adaptive weighting. Applied Mathematical Modelling 2021;95:503-23. [DOI: 10.1016/j.apm.2021.02.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
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