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Osama Ahmad Alomari
Researcher at Universiti Sains Malaysia
Publications - 46
Citations - 1207
Osama Ahmad Alomari is an academic researcher from Universiti Sains Malaysia. The author has contributed to research in topics: Computer science & Metaheuristic. The author has an hindex of 14, co-authored 29 publications receiving 550 citations. Previous affiliations of Osama Ahmad Alomari include National University of Malaysia & University of Sharjah.
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Journal ArticleDOI
Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering
TL;DR: Three meta-heuristic algorithms are adapted to solve the feature selection problem and a new dynamic dimension reduction (DDR) method is provided to reduce the number of features used in clustering and thus improve the performance of the algorithms.
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Bat-inspired algorithms with natural selection mechanisms for global optimization
Mohammed Azmi Al-Betar,Mohammed A. Awadallah,Hossam Faris,Xin-She Yang,Ahamad Tajudin Khader,Osama Ahmad Alomari +5 more
TL;DR: The results show that the bat-inspired versions with various selection schemes observing the “survival-of-the-fittest” principle are largely competitive to established methods.
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Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.
Zaid Abdi Alkareem Alyasseri,Zaid Abdi Alkareem Alyasseri,Mohammed Azmi Al-Betar,Mohammed Azmi Al-Betar,Iyad Abu Doush,Iyad Abu Doush,Mohammed A. Awadallah,Mohammed A. Awadallah,Ammar Kamal Abasi,Ammar Kamal Abasi,Sharif Naser Makhadmeh,Sharif Naser Makhadmeh,Osama Ahmad Alomari,Karrar Hameed Abdulkareem,Afzan Adam,Robertas Damasevicius,Mazin Abed Mohammed,Raed Abu Zitar +17 more
TL;DR: A comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis is provided in this article, which includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier.
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Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization
TL;DR: This study introduces a new method for improving the search capability of CSA by combining it with the bat algorithm (BA) to solve numerical optimization problems and indicates that CSBA performs better than the standard CSA and existing methods in the literature, particularly in terms of local search functions.
Journal ArticleDOI
Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
Osama Ahmad Alomari,Sharif Naser Makhadmeh,Sharif Naser Makhadmeh,Sharif Naser Makhadmeh,Mohammed Azmi Al-Betar,Mohammed Azmi Al-Betar,Zaid Abdi Alkareem Alyasseri,Zaid Abdi Alkareem Alyasseri,Iyad Abu Doush,Iyad Abu Doush,Ammar Kamal Abasi,Ammar Kamal Abasi,Mohammed A. Awadallah,Mohammed A. Awadallah,Raed Abu Zitar +14 more
TL;DR: A new hybrid filter-wrapper approach using robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the top-ranked genes and it achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining datasets.