| Efficient Bug Triaging Using Text Mining. M Alenezi, K Magel, S Banitaan J. Softw. 8 (9), 2185-2190, 2013 | 103 | 2013 |
| Bug reports prioritization: Which features and classifier to use? M Alenezi, S Banitaan 2013 12th International Conference on Machine Learning and Applications 2 …, 2013 | 72 | 2013 |
| A Comparative Analysis of Soft Computing Techniques for Predicting Software Effort Based Use Case Points M Azzeh, A Nassif, S Banitaan IET Software 12 (1), 19-29, 2018 | 55 | 2018 |
| Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation M Azzeh, AB Nassif, S Banitaan, F Almasalha NEURAL COMPUTING AND APPLICATIONS, 2015 | 47 | 2015 |
| Tram: An approach for assigning bug reports using their metadata S Banitaan, M Alenezi 2013 Third International Conference on Communications and Information …, 2013 | 43 | 2013 |
| Using data mining to predict possible future depression cases K Daimi, S Banitaan International Journal of Public Health Science (IJPHS) 3 (4), 231-240, 2014 | 36 | 2014 |
| Selecting discriminating terms for bug assignment: a formal analysis I Aljarah, S Banitaan, S Abufardeh, W Jin, S Salem Proceedings of the 7th International Conference on Predictive Models in …, 2011 | 33 | 2011 |
| Transformed k-nearest neighborhood output distance minimization for predicting the defect density of software projects C López-Martín, Y Villuendas-Rey, M Azzeh, AB Nassif, S Banitaan Journal of Systems and Software 167, 110592, 2020 | 21 | 2020 |
| Motivations for using social media: Comparative study based on cultural differences between American and Jordanian students H Al-Quraan, E Abu-Shanab, S Banitaan, H Al-Tarawneh International Journal of Social Media and Interactive Learning Environments …, 2017 | 21 | 2017 |
| User movement prediction: The contribution of machine learning techniques S Banitaan, M Azzeh, AB Nassif 2016 15th IEEE International Conference on Machine Learning and Applications …, 2016 | 19 | 2016 |
| Support vector regression for predicting the enhancement duration of software projects C Lopez-Martin, S Banitaan, A Garcia-Floriano, C Yanez-Marquez 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 17 | 2017 |
| Upsilon-SVR polynomial kernel for predicting the defect density in new software projects C López-Martín, M Azzeh, A Bou-Nassif, S Banitaan 2018 17th IEEE International Conference on Machine Learning and Applications …, 2018 | 16 | 2018 |
| Decoba: Utilizing developers communities in bug assignment S Banitaan, M Alenezi 2013 12th International Conference on Machine Learning and Applications 2, 66-71, 2013 | 16 | 2013 |
| Automatic identification of Chagas disease vectors using data mining and deep learning techniques Z Parsons, S Banitaan Ecological Informatics, 2021 | 15 | 2021 |
| Using categorical features in mining bug tracking systems to assign bug reports M Alenezi, S Banitaan, M Zarour arXiv preprint arXiv:1804.07803, 2018 | 15 | 2018 |
| Guest editorial: special issue on predictive analytics using machine learning AB Nassif, M Azzeh, S Banitaan, D Neagu Neural Computing and Applications 27, 2153-2155, 2016 | 14 | 2016 |
| A better case adaptation method for case-based effort estimation using multi-objective optimization M Azzeh, AB Nassif, S Banitaan 2014 13th International Conference on Machine Learning and Applications, 409-414, 2014 | 12 | 2014 |
| An Application of Classification and Class Decomposition to Use Case Point Estimation Method M Azzeh, AB Nassif, S Banitaan 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 11 | 2015 |
| A partitioning-based approach for robot path planning problems CY Wang, S Banitaan, J Lyu 2018 18th International Conference on Control, Automation and Systems (ICCAS …, 2018 | 10 | 2018 |
| Class Decomposition using K-means and Hierarchical Clustering S Banitaan, AB Nassif, M Azzeh 2015 IEEE 14th International Conference on Machine Learning and Applications, 2015 | 10 | 2015 |
Mohammad AzzehPrincess Sumaya University for Technology在 psut.edu.jo 的电子邮件经过验证
