Muhammad Sharif

Muhammad Sharif PhD (IEEE Senior Member), is an Associate Professor at COMSATS University Islamabad, Wah Cantt Pakistan. He has completed his PhD in Image Processing from COMSATS Institute of IT, Islamabad in 2013. He got his MS (CS) degree from the same institute in 2007 and MCS degree from Quaid-e-Azam University, Islamabad Pakistan in 1995. He has worked one year in Alpha Soft UK based software house as a developer/team leader in 1995. He is an Oracle Certified Professional in Developer Track. He has adopted teaching profession since 1996 to date. He has more than 150 research publications in IF, SCI and ISI journals as well as in national and international conferences and obtained 100+ Impact Factor. He has been supervised 40 MS (CS) thesis till date. Currently, he is supervising 05 PhD (CS) students and co-supervising 10 PhD (CS) students. Recently, one of his student successfully defended PhD (CS) thesis. In addition, more than 350 undergraduate students have successfully completed their degree project work under his supervision. His research interests include Medical Imaging, Biometrics, Computer Vision, Machine Learning and Agriculture/Plants imaging. He is being awarded with COMSATS Research Productivity Awards continuously since 2010 till now. He has been served in the TPC for the IEEE FIT 2013-17. He is also currently serving as an Associate Editor for IEEE Access Journal and Guest Editor in four Journal special issues as well as reviewer for many well reputed journals. He has headed the Department of Computer Sciences COMSATS Institute of IT, Wah Cantt from 2008 to 2011 and successfully achieved the targeted outputs. He also attended a number of prestigious national and international conferences throughout his career.

Publications

Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques
Mohammad Irfan, Seifedine Kadry, Muhammad Sharif, Habib Ullah Khan. © 2024. 334 pages.
In the realm of Islamic finance, a pivotal challenge looms—the escalating complexity of investment decisions, macroeconomic analyses, and credit evaluations. In response, we...
A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition
Muhammad Sharif, Muhammad Attique, Muhammad Zeeshan Tahir, Mussarat Yasmim, Tanzila Saba, Urcun John Tanik. © 2020. 26 pages.
Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on...