M
Mohammad Tubishat
Researcher at Information Technology University
Publications - 21
Citations - 992
Mohammad Tubishat is an academic researcher from Information Technology University. The author has contributed to research in topics: Local optimum & Feature selection. The author has an hindex of 8, co-authored 17 publications receiving 320 citations. Previous affiliations of Mohammad Tubishat include Asia Pacific University of Technology & Innovation & Yarmouk University.
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
TL;DR: An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode and demonstrates that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets.
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
TL;DR: The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
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Dynamic Salp swarm algorithm for feature selection
Mohammad Tubishat,Mohammad Tubishat,Salinah Jaafar,Mohammed Alswaitti,Seyedali Mirjalili,Norisma Idris,Maizatul Akmar Ismail,Mardian Shah Omar +7 more
TL;DR: The proposed Dynamic Salp swarm algorithm (DSSA) outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed.
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Implicit aspect extraction in sentiment analysis
TL;DR: This research provides a review of implicit aspect/features extraction techniques from different perspectives by making a comparison analysis for the techniques available for implicit term extraction with a brief summary of each technique.
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An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field
Zenab Mohamed Elgamal,Norizan Mohd Yasin,Mohammad Tubishat,Mohammed Alswaitti,Seyedali Mirjalili +4 more
TL;DR: A novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed, which confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.