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Abubakar Elsafi
Researcher at Universiti Teknologi Malaysia
Publications - 12
Citations - 191
Abubakar Elsafi is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Cloud computing & Integration testing. The author has an hindex of 5, co-authored 11 publications receiving 142 citations. Previous affiliations of Abubakar Elsafi include Future University in Egypt & International University of Africa.
Papers
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Journal ArticleDOI
Quality of service approaches in cloud computing
TL;DR: In this paper, the authors conducted a systematic mapping study to find the related literature, and 67 articles were selected as primary studies that are classified in relation to the focus, research type and contribution type.
Journal ArticleDOI
Stakeholder management in value-based software development: systematic review
TL;DR: The aim is to find out the reported evidence based attributes or characteristics of the stakeholders and their usage context in terms of their application in different domains, stakeholders' quantification metrics, the reported stakeholder types and the reported issues of VBS development.
Journal ArticleDOI
A systematic mapping study on solving university timetabling problems using meta-heuristic algorithms
Abeer Bashab,Ashraf Osman Ibrahim,Ashraf Osman Ibrahim,Eltayeb E. AbedElgabar,Mohd Arfian Ismail,Abubakar Elsafi,Ali Ahmed,Ajith Abraham +7 more
TL;DR: The result of this study confirmed the efficiency and intensive application of the meta-heuristic algorithms in solving university timetabling problems, specifically the hybrid algorithms.
Proceedings ArticleDOI
A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction
Abdullahi Uwaisu Muhammad,Adamu Sani Yahaya,Suhail Muhammad Kamal,Jibril Muhammad Adam,Wada Idris Muhammad,Abubakar Elsafi +5 more
TL;DR: The experimental results obtained from this research work indicates the coupled (Stacked LSTM+GRU) with supervised learning to significantly outperform the authors' reference models for water price Prediction.
Book ChapterDOI
Classification of Mammogram Images Using Radial Basis Function Neural Network
Ashraf Osman Ibrahim,Ali Ahmed,Aleya Abdu,Rahma Abd-alaziz,Mohamed Alhaj Alobeed,Abdulrazak Yahya Saleh,Abubakar Elsafi +6 more
TL;DR: This paper presents the classification method for mammogram Image using Radial Basis Function Network (RBF) technique, focused on features extracted from the breast cancer mammogram image processing algorithms, to illustrate the capability of the RBF network to obtain better classification accuracy results.