Mohammad Azzeh

Jordan Applied Science Private University

ORCID
Published in 2017
Robust Rank Aggregation method for Case-Base effort estimation
Authors: -
Journal: Canadian Conference on Electrical and Computer Engineering
ORCID
Published in 2017
User movement prediction: The contribution of machine learning techniques
Authors: -
Journal: Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
ORCID
Published in 2016
A hybrid model for estimating software project effort from Use Case Points
Authors: Mohammad Azzeh, Ali Bou Nassif
Journal: Applied Soft Computing
ORCID
Published in 2016
An application of classification and class decomposition to use case point estimation method
Authors: -
Journal: Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
ORCID
Published in 2016
Class decomposition using K-means and hierarchical clustering
Authors: -
Journal: Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
ORCID
Published in 2016
Guest editorial: special issue on predictive analytics using machine learning
Authors: Ali Bou Nassif, Mohammad Azzeh, Shadi Banitaan, Daniel Neagu
Journal: Neural Computing and Applications
ORCID
Published in 2016
Neural network models for software development effort estimation: a comparative study
Authors: Ali Bou Nassif, Mohammad Azzeh, Luiz Fernando Capretz, Danny Ho
Journal: Neural Computing and Applications
ORCID
Published in 2016
Pareto efficient multi-objective optimization for local tuning of analogy-based estimation
Authors: Mohammad Azzeh, Ali Bou Nassif, Shadi Banitaan, Fadi Almasalha
Journal: Neural Computing and Applications
ORCID
Published in 2016
Preface
Authors: -
Journal: Proceedings - CSIT 2016: 2016 7th International Conference on Computer Science and Information Technology
ORCID
Published in 2016
User Movement Prediction: The Contribution of Machine Learning Techniques
Authors: -
Journal: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)