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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Journal ArticleDOI
Efficient Hybrid Nature-Inspired Binary Optimizers for Feature Selection
Majdi Mafarja,Asma Qasem,Ali Asghar Heidari,Ali Asghar Heidari,Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili +6 more
TL;DR: An enhanced hybrid metaheuristic approach using grey wolf optimizer and whale optimization algorithm to develop a wrapper-based feature selection method that outperforms other state-of-the-art approaches, significantly.
Journal ArticleDOI
A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index
TL;DR: This paper explores the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P 500 stock index and shows how traditional models such as multiple linear regression (MLR) behave in this case.
Journal ArticleDOI
Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts
TL;DR: A hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks and provides very challenging results in terms of precision, recall, f-measure and AUC.
Book ChapterDOI
Salp swarm algorithm: Theory, literature review, and application in extreme learning machines
TL;DR: The application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method.
Journal ArticleDOI
Training radial basis function networks using biogeography-based optimizer
TL;DR: The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima.