Mr. Abdul Jabbar
Mr. Abdul Jabbar
I come from a rural region of Pakistan, where access to advanced educational and technological opportunities was limited. This background inspired me to pursue higher education and contribute to meaningful technological advancements. I completed my undergraduate studies in Electrical Engineering, where I developed a strong foundation in engineering principles and problem-solving. During my master’s degree, I became fascinated by Artificial Intelligence and its potential to address real-world challenges, particularly in healthcare and engineering. This interest motivated me to pursue further research in AI, machine learning, and data-driven technologies, with the goal of developing innovative solutions that can create a positive societal impact.
Qualifications
- Bachelor of Engineering , University of Engineering & Technology, Peshawar, Pakistan
- Master by Research , University of Engineering & Technology, Peshawar, Pakistan
Expertise
- Biomedical Signal Processing, Medical Imaging, AI in Healthcare
I was awarded the Gold Medal in both my Bachelor’s and Master’s degrees for achieving the highest academic performance in my respective programs. I was also recognized as the top graduating student at the university level in both degrees, reflecting my commitment to academic excellence and lifelong learning.
Research Interests
My research interests include artificial intelligence, machine learning, data science, signal processing, biomedical engineering, and digital health. I am interested in developing innovative data-driven technologies that address real-world challenges and support advancements in healthcare, engineering, and interdisciplinary research.
- Abdul Jabbar, Ethan Grooby, Yang Yi Poh, Khawza I. Ahmad, Md Hassanuzzaman, Raqibul Mostafa, Ahsan H. Khandoker, Faezeh Marzbanrad,
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion,
Computers in Biology and Medicine, Volume 197, Part A, 2025, 110993, ISSN 0010-4825,
https://doi.org/10.1016/j.compbiomed.2025.110993. - Naeem Khan, Abdul Jabbar, Hazrat Bilal, Umar Gul, Compensated closed-loop Kalman filtering for nonlinear systems, Measurement, Volume 151, 2020, 107129, ISSN 0263-2241,
https://doi.org/10.1016/j.measurement.2019.107129. - Ullah, A., Yousaf, M.Z., Aziz, A. et al. Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique. Discov Artif Intell 5, 84 (2025). https://doi.org/10.1007/s44163-025-00282-0
- Jabbar, Abdul, M. Irfan Khtaak, and Arif Ullah. “Using Computer Tomographic (CT) Images A Robust Hybrid Computer-Aided Deep Learning Framework for Lung Cancer Classification.” International Journal of Engineering Works 10.06 (2023): 55-63.
- Jabbar, A., Grooby, E., Crozier, J., Gallon, A., Pham, V., Ahmad, K. I., Hassanuzzaman, M., Mostafa, R., Khandoker, A. H., & Marzbanrad, F. (2025). Congenital heart disease classification using phonocardiograms: A scalable screening tool for diverse environments (arXiv:2503.22773). arXiv. https://arxiv.org/abs/2503.22773
- S. S. Raoof, M. A. Jabbar and S. A. Fathima, “Lung Cancer Prediction using Machine Learning: A Comprehensive Approach,” 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 2020, pp. 108-115, doi: 10.1109/ICIMIA48430.2020.9074947.
keywords: {Lung;Cancer;Prediction algorithms;Support vector machines;Computed tomography;Solid modeling;Classification algorithms;Lung Cancer;Machine Learning;SVM;ANN;NLP}, - M. A. Arshed, W. Qureshi, M. U. G. Khan and M. A. Jabbar, “Symptoms Based Covid-19 Disease Diagnosis Using Machine Learning Approach,” 2021 International Conference on Innovative Computing (ICIC), Lahore, Pakistan, 2021, pp. 1-7, doi: 10.1109/ICIC53490.2021.9692986.
keywords: {COVID-19;Pandemics;Computational modeling;Machine learning;Predictive models;Data models;Coronaviruses;COVID-19 Symptoms;COVID-19 Disease Diagnosis;Machine Learning;RT-PCR Test}, - Junaid, M., Iqbal, M., Ragab, A.H. et al. Advanced theoretical insights into energetically favorable and structurally stable boron-doped graphitic carbon nitride for effective catalytic removal of nitrous oxide and carbon monoxide from industrial flue gases. Environ Sci Pollut Res 32, 14928–14943 (2025). https://doi.org/10.1007/s11356-025-36554-6
Supervision
PHD
Dr. Faezeh Marzbanrad
Heart murmur detection from Phonocardiogram Signals
2024 to Till Date
Prof. Mehmet R. Yuce
Heart murmur detection from Phonocardiogram Signals
2024 to Till Date
Prof. Prof. Ahsan Habib Khandoker
Heart murmur detection from Phonocardiogram Signals
2024 to Till Date
Masters
Prof. Muhammad Irfan Khattak
Lung Cancer classification using deep learning
2020 to 2022
Undergraduate
Prof. Naeem Khan
Compensated closed kalman filtering for nonlinear systems
2015 to 2019
Teaching Commitments
- ECE2111 - Signals and systems
- ECE2191 - Probability and AI for engineers
- ECE4076 - Computer Vision
- ECE4179 - Neural networks and deep learning