Dr. Muhammad Nouman Noor

Faculty Member

Dr. Muhammad Nouman Noor

Assistant Professor

HEC Approved PhD Supervisor
(+9251 111 128 128) 3505
nouman.noor@isb.nu.edu.pk

Education

PhD (CS), HITEC University Taxila, 2023

MS (CS), Bahria University Islamabad, 2019

BS (CS), PMAS-Arid Agriculture University, 2014

Summary

Dr. Muhammad Nouman Noor is currently an Assistant Professor at Fast-National University of Computer and Emerging Sciences, Islamabad. He has an overall job experience (including the software industry, research, and academia) of around 11 years. Leveraging over 11 years of experience, including 5+ years in the software industry, with diverse project contributions, the focus has seamlessly transitioned to academia. However, collaborations with industry on projects continue, accumulating 5+ years of experience in teaching, research, and mentorship. He did his Ph.D. in Computer Science from HITEC University, Taxila, Pakistan. He has a total of 18 research publications in journals and conferences of international repute, and 2 book chapters, reflecting expertise in computer vision, deep learning, digital image processing, and related domains. Demonstrated leadership through the supervision of numerous software projects and master's theses, fostering practical skills and academic growth. He has also supervised more than 30 R&D projects as well as software projects of undergraduate students. Proficient in collaborating across multidisciplinary teams, delivering high-quality educational content, and providing valuable insights for real-world computer science challenges. A dynamic and resourceful individual who bridges the gap between practical IT industry problems and advanced academic research.

Publications

Journal Papers

  1. Hafiz Muhammad Awais Riaz, Muhammad Nouman Noor, Asad Ali Safi, Ahmad Raza Shahid, Improving Inference Time in Diabetic Retinopathy-Recent Trends and Future Directions, IEEE Access
  2. Muhammad Nouman Noor, Farah Haneef, Imran Ashraf, Muhammad Masud, Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach, Bioengineering
  3. Muhammad Nouman Noor, Muhammad Nazir, Sajid Ali Khan, Oh-Young Song, Imran Ashraf, Efficient gastrointestinal disease classification using pretrained deep convolutional neural network, Electronics
  4. Muhammad Nouman Noor, Muhammad Nazir, Imran Ashraf, Nouf Abdullah Almujally, Muhammad Aslam, Syeda Fizzah Jilani, GastroNet: A robust attention‐based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images, CAAI Transactions on Intelligence Technology
  5. Tehmina Karamat Mousa Alhajlah, Muhammad Nouman Noor, Muhammad Nazir, Awais Mahmood, Imran Ashraf, Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization, Computers, Materials and Continua
  6. Muhammad Nouman Noor, Muhammad Nazir, Sajid Ali Khan, Imran Ashraf, Oh-Young Song, Localization and classification of gastrointestinal tract disorders using explainable AI from endoscopic images, Applied Sciences
  7. Farah Haneef, Rabeeh Ayaz Abbasi, Muddassar Azam Sindhu, Akmal Saeed Khattak, Muhammad Nouman Noor, Naif Radi Aljohani, Ali Daud, Sachi Arafat, Using network science to understand the link between subjects and professions, Computers in Human Behavior
  8. Muhammad Nouman Noor, Muhammad Nazir, Imran Ashraf, Emerging trends and advances in the diagnosis of gastrointestinal diseases, BioScientific Review
  9. Muhammad Nouman Noor, Muhammad Khalid, Yousra Zafar, Hira Khalid, The Impact of Al on Society: A Multifaceted Ethical Examination, INTERNATIONAL JOURNAL OF ISLAMIC STUDIES AND CULTURE (IJISC)
  10. Muhammad Nouman Noor, Tamim Ahmed Khan, Farah Haneef, Muhammad Ismail Ramay, Machine learning model to predict automated testing adoption, International Journal of Software Innovation (IJSI)
  11. Abdul Samad Danish, Nouman Noor, Yasir Hamid Onib-Ur-Rehman, Hina Ali Khan, Rana Muneeb Asad, Agha Muhammad Yar Khan, Augmented Narratives: Unveiling the Efficacy of Storytelling in Augmented Reality Environments, Journal of Xi’an Shiyou University, Natural Science Edition
  12. Fatima Waseem, Farah Haneef, Rabeeh Ayaz Abbasi, Muhammad Nouman Noor, Aisha Khalid, Identifying Significant Factors Associated with Career Selection: A survey based study in Pakistan, Pakistan Journal of Engineering, Technology & Science (PJETS)
  13. Fatima Waseem, Farah Haneef, Muhammad Nouman Noor, Aisha Khalid, Hina Rashid, & Qudsia Yousaf, A Review based on Active Research Areas in Mining Software Bug Repositories: Limitations and Possible Future Trends, International Journal of Innovations in Science & Technology (IJIST)
  14. Farah Haneef, Rabeeh Ayaz Abbasi, Muhammad Nouman Noor, Ali Daud, Mudassar Azam Sindhu, A Review of Career Selection Models, Researchpedia Journal of Computing
  15. Muhammad Nouman Noor, Farah Haneef, A Review on Big Data and Social Network Analytics Techniques, Researchpedia Journal of Computing

Conference Papers

  1. Muhammad Nouman Noor, Maryam Sana, Farah Haneef, Imran Ashraf, Akhtar Jamil, Alaa Ali Hameed, Analysis of Dermoscopic Images for Classification of Skin Lesions Using Advanced Image Processing Techniques, 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI)
  2. Muhammad Nouman Noor, Muhammad Nazir, Saad Rehman, Junaid Tariq, Sketch-recognition using pre-trained model, National Conference on Engineering and Computing Technology, Islamabad, Pakistan
  3. Muhammad Nouman Noor, Muhammad Nazir, Veena Dilshad, Farah Haneef, A Framework for Multi-Grade Classification of Ulcerative-Colitis Using Deep Neural Networks, 25th International Multitopic Conference (INMIC)

Books & Chapters

  1. Muhammad Nouman Noor, Saddaf Rubab, Saad Rehman, Machine Learning Techniques in Conjunction with Data Science Applications for CR-IoV, Cognitive Radio-based Internet of Vehicles (CRC Press)
  2. Muhammad Nouman Noor, Imran Ashraf, Muhammad Nazir, Analysis of GAN-based data augmentation for GI-tract disease classification, Advances in Deep Generative Models for Medical Artificial Intelligence