Associate Professor jyousif@su.edu.om Phone: +968 26850100 Extn: 307 Location: Rustaq building, Office no: F2-08
 |  |  |
- Ph.D. In Information Science & Technology (Artificial Intelligent &NLP) -National University of Malaysia (UKM) 2007.
- M.Sc. Computer Science, Basra University -Iraq, 1996.
- B.Sc. Computer Science, Basra University -Iraq, 1991.
- Artificial Intelligence & Expert Systems.
- Soft computing and its applications
- Neural Networks & Fuzzy Logic.
- Modeling & Visualization.
- Computer Graphics.
- System analysis & Design.
- Computer Simulation &Modeling.
- Compilers Construction.
- Data Structures with C#, C++, Pascal.
- Data Base Management System.
- Object oriented programming with C#.
- Introduction to Information Technology.
- Fundamentals of programming.
- Introduction to Computer Science & programming.
- Intelligent Agent
- Soft Computing
- Renewable energy
- Forecasting Models
- Cloud Computing, Artificial Neural Networks
- Natural Language Processing
- Virtual Reality
- Al-Waeli, A. H., Kazem, H. A., Yousif, J. H., Chaichan, M. T., & Sopian, K. (2020). Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance. Renewable Energy, 145, 963-980.
- Kazem, H. A., Yousif, J., Chaichan, M. T., & Al‐Waeli, A. H. (2019). Experimental and deep learning artificial neural network approach for evaluating grid‐connected photovoltaic systems. International Journal of Energy Research, 43(14), 8572-8591.
- Al‐Waeli, A. H., Kazem, H. A., Yousif, J. H., Chaichan, M. T., & Sopian, K. (2019). Mathematical and neural network models for predicting the electrical performance of a PV/T system. International Journal of Energy Research, 43(14), 8100-8117.
- Al-Waeli, A. H., Sopian, K., Yousif, J. H., Kazem, H. A., Boland, J., & Chaichan, M. T. (2019). Artificial neural network modeling and analysis of photovoltaic/thermal system based on the experimental study. Energy conversion and management, 186, 368-379.
- Yousif, J. H., Kazem, H. A., Alattar, N. N., & Elhassan, I. I. (2019). A comparison study based on artificial neural network for assessing PV/T solar energy production. Case Studies in Thermal Engineering, 13, 100407.
