Hossam Faris

TL;DR: In this paper, a credibility domain-based KG Embedding framework is proposed to capture a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics domain ontology.

TL;DR: The results reported here support that CSA has achieved an outstanding performance in solving the problem of ELD in power systems, demonstrating their good optimization capabilities through arriving at a combination of power loads that consummate the constraints of the ELD problem while simultaneously lessening the entire fuel cost.

TL;DR: Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

TL;DR: A parallel heterogeneous ensemble feature selection based on three well-regarded algorithms: genetic algorithm, particle swarm optimizer, and grey wolf optimizer is proposed, which shows that the proposed parallel approach improved the performance in terms of the prediction results and running time.

TL;DR: A churn prediction framework is proposed aiming at enhancing the ability to forecast customer churn, which surpasses various state-of-the-art classification methods for this particular dataset.