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Cited by in F6Publishing
For: Elaziz MA, Moemen YS, Hassanien AE, Xiong S. Quantitative Structure-Activity Relationship Model for HCVNS5B inhibitors based on an Antlion Optimizer-Adaptive Neuro-Fuzzy Inference System. Sci Rep 2018;8:1506. [PMID: 29367667 DOI: 10.1038/s41598-017-19122-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 1.8] [Reference Citation Analysis]
Number Citing Articles
1 Heidari AA, Faris H, Mirjalili S, Aljarah I, Mafarja M. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks. In: Mirjalili S, Song Dong J, Lewis A, editors. Nature-Inspired Optimizers. Cham: Springer International Publishing; 2020. pp. 23-46. [DOI: 10.1007/978-3-030-12127-3_3] [Cited by in Crossref: 33] [Cited by in F6Publishing: 4] [Article Influence: 11.0] [Reference Citation Analysis]
2 Abdi J, Hadipoor M, Esmaeili-Faraj SH, Vaferi B. A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids. Sci Rep 2022;12:4415. [PMID: 35292713 DOI: 10.1038/s41598-022-08304-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Hussein AM, Abd Elaziz M, Abdel Wahed MS, Sillanpää M. A new approach to predict the missing values of algae during water quality monitoring programs based on a hybrid moth search algorithm and the random vector functional link network. Journal of Hydrology 2019;575:852-63. [DOI: 10.1016/j.jhydrol.2019.05.073] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 2.7] [Reference Citation Analysis]
4 Anter AM, Moemen YS, Darwish A, Hassanien AE. Multi-target QSAR modelling of chemo-genomic data analysis based on Extreme Learning Machine. Knowledge-Based Systems 2020;188:104977. [DOI: 10.1016/j.knosys.2019.104977] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
5 Houssein EH, Neggaz N, Hosney ME, Mohamed WM, Hassaballah M. Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities. Neural Comput & Applic 2021;33:13601-18. [DOI: 10.1007/s00521-021-05991-y] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
6 Ibrahim RA, Elaziz MA, Lu S. Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization. Expert Systems with Applications 2018;108:1-27. [DOI: 10.1016/j.eswa.2018.04.028] [Cited by in Crossref: 80] [Cited by in F6Publishing: 12] [Article Influence: 20.0] [Reference Citation Analysis]
7 Margin OC, Dulf EH, Mocan T, Mocan L. Modelling in Synthesis and Optimization of Active Vaccinal Components. Nanomaterials (Basel) 2021;11:3001. [PMID: 34835765 DOI: 10.3390/nano11113001] [Reference Citation Analysis]
8 Pathak VK, Gangwar S, Singh R, Srivastava AK, Dikshit M. A comprehensive survey on the ant lion optimiser, variants and applications. Journal of Experimental & Theoretical Artificial Intelligence. [DOI: 10.1080/0952813x.2022.2093409] [Reference Citation Analysis]