H
Hossam Faris
Researcher at University of Jordan
Publications - 185
Citations - 15014
Hossam Faris is an academic researcher from University of Jordan. The author has contributed to research in topics: Metaheuristic & Feature selection. The author has an hindex of 40, co-authored 178 publications receiving 7977 citations. Previous affiliations of Hossam Faris include University of Salento.
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Improving Extreme Learning Machine by Competitive Swarm Optimization and its application for medical diagnosis problems
TL;DR: A new model that uses Competitive Swarm Optimizer (CSO) to optimize the values of the input weights and hidden neurons of ELM to increase the generalization performance, stabilize the classifier, and to produce more compact networks by reducing the number of neurons in the hidden layer is proposed.
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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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River suspended sediment load prediction based on river discharge information: application of newly developed data mining models
Sinan Q. Salih,Ahmad Sharafati,Khabat Khosravi,Hossam Faris,Ozgur Kisi,Hai Tao,Mumtaz Ali,Zaher Mundher Yaseen +7 more
TL;DR: The current and one-day lead time river flow and sediment load were the influential predictors for one- day-ahead SSL prediction, and the M5P model gave a superior prediction result.
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Natural selection methods for Grey Wolf Optimizer
Mohammed Azmi Al-Betar,Mohammed A. Awadallah,Hossam Faris,Ibrahim Aljarah,Abdelaziz I. Hammouri +4 more
TL;DR: Six versions of GWO are proposed and TGWO achieved the best results for almost all benchmark functions, proving that the selection methods have a high impact on the performance ofGWO.
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Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach
Ibrahim Aljarah,Majdi Mafarja,Ali Asghar Heidari,Ali Asghar Heidari,Hossam Faris,Seyedali Mirjalili +5 more
TL;DR: The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.