Assist Prof Dr. Muhammad Ishaq, The University of Agriculture Peshawar, Pakistan
Dr. Muhammad Ishaq earned his PhD in Computer Science with Distinction from Harbin Engineering University as an HEC Scholar in 2012. With 12 years of post-PhD teaching experience, he has significantly contributed to academia by organizing conferences and launching programs like BS (Bioinformatics), BS (Artificial Intelligence), MS (Data Science), and PhD (Computer Science). Dr. Ishaq has played a pivotal role in enhancing curricula and spearheading university computerization projects. He manages the HEC’s Digital Learning and Skills Enrichment Initiative (DLSEI) and has published numerous high-quality research papers. His dedication to supervising research theses and submitting projects to funding agencies showcases his commitment to excellence. šāØ
Publication Profile
Scopus
š„ļø Academic Background š
Dr. Muhammad Ishaq earned a PhD in Computer Science with Distinction from Harbin Engineering University as an HEC Scholar in 2012.
Research Focus
Dr. Muhammad Ishaq’s research focuses on machine learning, neural networks, and optimization algorithms. He has made significant contributions to data imputation in categorical datasets, robust crowd counting, and medical data classification. His work also includes optimizing neural network weights using accelerated particle swarm optimization and improving task scheduling for computational alignment of biological sequences. Dr. Ishaq’s research in agri-informatics and wireless body area networks further highlights his diverse expertise. His publications in esteemed journals and conference papers reflect his dedication to advancing computational methods and artificial intelligence. šš¤š”
Publication Top Notes
- Machine Learning Based Missing Data Imputation in Categorical Datasets (Ishaq, M., et al., IEEE Access, 2024) – ššµļøāāļø
- Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet (Zahir, S., et al., Computer Systems Science and Engineering, 2023) – ššµļøāāļø2 citations
- NUMERICAL SOLUTION of WAVELET NEURAL NETWORK LEARNING WEIGHTS USING ACCELERATED PARTICLE SWARM OPTIMIZATION ALGORITHM (Zeb, A., et al., Fractals, 2023) – ššµļøāāļø1 citation
- Optimizing connection weights of functional link neural network using APSO algorithm for medical data classification (Khan, A., et al., Journal of King Saud University – Computer and Information Sciences, 2022) – ššµļøāāļø11 citations
- A dynamic swift association scheme for wireless body area networks (Sheraz, A., et al., Transactions on Emerging Telecommunications Technologies, 2022) – ššµļøāāļø
- Comprehensive selective improvements in agri-informatics semantics (Ishaq, M., et al., Journal of Information Science, 2022) – ššµļøāāļø1 citation
- Smart Control System for User Confirmation Based on IoT (Khan, A., et al., Lecture Notes in Networks and Systems, 2022) – ššµļøāāļø
- An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences (Ishaq, M., et al., Computational and Mathematical Methods in Medicine, 2022) – ššµļøāāļø1 citation
- Current Trends and Ongoing Progress in the Computational Alignment of Biological Sequences (Ishaq, M., et al., IEEE Access, 2019) – ššµļøāāļø3 citations
- Cognition in a cognitive routing system for mobile ad-hoc network through leaning automata and neural network (Afridi, M.I., et al., Applied Mechanics and Materials, 2013) – ššµļøāāļø