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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Proceedings ArticleDOI
Forecasting global carbon dioxide emission using auto-regressive with eXogenous input and evolutionary product unit neural network models
TL;DR: Two types of Artificial Neural Networks (ANN) models are developed for forecasting the world CO2 emission based on the global energy consumption using the Neural Network Auto-Regressive with eXogenous (ARX) Input model named as (NNARX).
Journal ArticleDOI
Classification of Arabic healthcare questions based on word embeddings learned from massive consultations: a deep learning approach
TL;DR: In this paper, the authors proposed a deep learning approach for question classification, since deep learning methods have the powerful capability to extract implicit, hidden relationships and automatically generate dense representations of features.
Proceedings ArticleDOI
The Influence of Input Data Standardization Methods on the Prediction Accuracy of Genetic Programming Generated Classifiers.
Amaal R. Al Shorman,Hossam Faris,Pedro A. Castillo,Juan Julián Merelo Guervós,Nailah Al-Madi +4 more
TL;DR: The results showed that the computational efficiency of GP is highly enhanced when coupled with some standardization methods, specifically Min-Max method for scenario I and Vector method for scenarios II, and scenario III, and whereas, Manhattan and Z-Score methods had the worst results for all three scenarios.
Posted ContentDOI
Robotics Evolution: from Remote Brain to Cloud.
TL;DR: This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend and offers promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era.
Proceedings ArticleDOI
Predicting Different Types of Imbalanced Intrusion Activities Based on a Multi-Stage Deep Learning Approach
TL;DR: In this article, the authors proposed a deep learning approach to detect different types of intrusion activities using a multistage mechanism and an oversampling process which solves the problem of the imbalanced data produced by the IoT devices.