Mohammed Akour
Yarmouk University
54 Papers
139 Citations
Mohammed Akour is an academic researcher from Yarmouk University. The author has contributed to research in topics: Test suite & Software. The author has an hindex of 8, co-authored 48 publications. Previous affiliations of Mohammed Akour include North Dakota State University & College of Information Technology.
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Papers
The Need of Integrating Digital Education in Higher Education: Challenges and Opportunities
TL;DR: In this paper , the future potential and difficulties of information and communication technology (ICT) and digital education as they relate to adopting the most recent technological advancements in the digital era and extensive online open courses.
The Influence of Deep Learning Algorithms Factors in Software Fault Prediction
TL;DR: Two deep learning algorithms are studied, Multi-layer perceptron’s (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms, and the effect of modifying parameters had an important role in prediction performance.
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The Importance of Big Data Analytics in Business: A Case Study
Hiba Alsghaier,Mohammed Akour,Issa Shehabat,Samah Aldiabat +3 more
- 28 Sep 2017
TL;DR: This research highlights some aspects of Big Data and its importance on organizations' business performance and how companies can use the famous open source platform Hadoop to process data to gain the competitive advantage.
Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier
TL;DR: Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.
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Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods
TL;DR: Three symmetric ensemble methods: bagging, boosting and stacking are used to predict faulty modules based on evaluating the performance of 11 base learners and showed that the random forest classifier is one of the most significant classifiers that should be stacked with other classifiers to gain the better fault prediction.
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