Anas Quteishat

Work place: Faculty of Engineering Technology AlBalqa Applied University, Salt, Jordan



Research Interests: Computer systems and computational processes, Neural Networks, Decision Support System, Data Structures and Algorithms


Dr. Anas Quteishat finished his Ph.D. from the University of Science Malaysia, in computational intelligence in 2009. He joined the Faculty of Engineering Technology at Al Balqa Applied University Jordan in 2009 as an Assistant Professor. In 2013 he got promoted to Associate Professor through his great record of publication. In 2015 he joined Sohar University in Sultanate of Oman. Since 2017 he is the head of the department for the Electrical and Computer Engineering program in the faculty. He is also an external institute and program accreditation member with the Oman Academic Accreditation Authority. His research area focuses on artificial neural networks, fuzzy systems, intelligent agents, and decision support systems. He has published many journal articles in the aforementioned fields.

Author Articles
Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models

By Mukesh Kumar Nidhi Anas Quteishat Ahmed Qtaishat

DOI:, Pub. Date: 8 Jun. 2022

The purpose of this study is to conduct an empirical investigation and comparison of the effectiveness of various classifiers and ensembles of classifiers in predicting academic performance. The study will evaluate the performance and efficiency of ensemble techniques that employ several classifiers against the performance and efficiency of a single classifier. Reducing student attrition is a serious concern for educational institutions worldwide. Educators are looking for strategies to boost student retention and graduation rates. This is only achievable if at-risk students are appropriately recognized early on. However, most commonly used predictive models are inefficient and inaccurate due to intrinsic classifier limitations and the usage of minor factors. The study contributes to the body of knowledge by proposing the development of optimized ensemble learning model that can be used for improving academic performance prediction. Overall, the findings demonstrate that the approach of employing optimized ensemble learning (OEL) model approaches is extremely efficient and accurate in terms of predicting student performance and aiding in the identification of students who are in the fear of attrition.

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