Faculty and Researcher Abstracts
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Browsing Faculty and Researcher Abstracts by Author "Bettyeb, Said"
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Item Intervention-based Method for Improving Learning and Critical Thinking in Computer Science(Institute of Electrical and Electronic Engineers Computing Conference, 2017) Bettyeb, SaidA number of studies have shown that critical thinking does not yield directly with education; and therefore, extensive research in the past two decades have focused on introducing and infusing critical thinking skills in higher education. It is agreed that acquiring critical thinking skills is of utmost significance in higher education. Also several studies have shown that academic and classroom interventions are effective in improving learning and academic performance of students. In this paper, we study some learning strategies and interventions and examine their effect on improving learning and critical thinking in higher education. Specifically, we examine the effectiveness of carefully designed subject matter instructional interventions to improve and promote critical thinking skills in graduate computing courses. The evaluation results of this study are very encouraging and show that our interventions have positive effect in improving critical thinking skills among studentsItem On the K-ary Hypercube(Theoretical Computer Science Journal, 1995) Bettyeb, SaidIn this paper, we propose and analyze a new interconnection network, the k -ary hypercube. This new architecture captures the advantages of the mesh network and those of the binary hypercube. We show that the hamiltoniacity of this network and its capability of efficiently simulating other topologies. It has a smaller degree than that of its equivalent binary hypercube (the one with at least as many nodes) and has a smaller diameter than its equivalent mesh of processors.Item Protein Interactive-based Features' Extraction(International Journal of Bioinformatics Research and Application, 2016) Bettyeb, SaidProtein and DNA features extraction represents an interesting research subject for a wide range of relevant applications. In this paper, we evaluated interactions in genes and diseases by modelling them as social networks. We introduced weighted cliques to indicate patterns of those interactions and distinguish the relations between different vertices based on their strengths or levels of interactions. We used those patterns as features and evaluate their value in comparison with other feature extraction existing approaches.