Adnan Qureshi

Biography

Adnan N. Qureshi received the Ph.D. degree from the Institute for Research in Applicable Computing (IRAC), University of Bedfordshire, U.K. With more than 21 years of teaching experience in higher education, both in Pakistan and U.K., he has been part of MyHealthAvatar and CHIC Projects, funded by the EU, during his job at the Centre for Computer Graphics and Vision (CCGV), University of Bedfordshire, UK. His academic contributions include publications in peer-reviewed conference and journals, supervision of Ph.D., M.Phil. and undergraduate theses and dissertations. As Director of Centre for Applied Data Analytics (CADA), University of Central Punjab, Pakistan, he has established research collaborations with several universities in UK, Malaysia, Italy, Saudi Arabia and China in areas of healthcare informatics and modelling, adaptive learning & assessment, optimisation techniques and artificial intelligence.

Profile

Administrative Responsibilities

  • April 2016 to June 2023, Associate Professor and Head of Department (Computer Science), Faculty of IT&CS, University of Central Punjab, Lahore, Pakistan.
  • 2004 to Dec. 2011, Assistant Professor and Head of Department of Computer Science, University of South Asia, Lahore, Pakistan.
  • 2000 to Sept. 2004, Assistant Professor and Head of Department, Computer Science & IT, Iqra University, Lahore Campus, Pakistan.

Other Activities

Conferences and Other Research Activity

Ph.D. supervision as 1st supervisor:

  1. Saeed Iqbal, Ph.D. CS, Adaptive Self-learning Systems for Medical Image Analysis using Hybrid-Dense Convolutional Neural Network, 2023, passed and completed.
  2. Imran Arshad Choudhry, Ph.D. CS, A Hybrid Approach of Feature Engineering and Convolutional Neural Networks for Robust Medical Image Segmentation, 2023, passed and completed.
  3. Kazim Ali, Ph.D. CS, Improving Robustness of Convolutional Neural Network against Adversarial Attacks in Computer Vision, 2022, passed and completed.

M.Phil. supervision as 1st supervisor:

  1. Bilal Ishfaq, M.Phil. CS, Hybridization of Manually Extracted and Convolutional Features for Improving Robustness of Classification of Chest X-Ray of COVID-19, 2023, passed.
  2. Mehwish Asghar, M.Phil. CS, Comparison and Evaluation of Convolutional versus Manually Extracted Features for Analyses of Breast Cancer Images, 2021, passed and completed.
  3. Qurrat ul Ain, M.Phil. CS, A combined approach to increase accuracy in predicting players performance in ODI, 2020, passed and completed.
  4. Ammar A. Fareed, M.Phil. CS, Evaluation of Feature based Medical Image Registration for Longitudinal and Cross-sectional studies, 2020, passed and completed.
  5. Usman Sharif, M.Phil. CS, ORTIA: An Algorithm to Improve Quality of Experience in HTTP Adaptive Bitrate Streaming Sessions, 2020, passed and completed.
  6. Maliha Ateeq, M.Phil. CS, Computer-Aided Foetal Heart Monitoring through Phonocardiography, 2019, passed and completed.
  7. Muhammad F. Khan, M.Phil. CS, PCG Analysis using ANN with Multi-Features, 2019, passed and completed.

Undergraduate project supervision as 1st supervisor:

  1. Ayesha Waris, BSCS, Chest x-ray abnormalities detection using CNN, 2022, passed and completed.
  2. Ali Sehran, BSCS, Real-time landmark recognition for tourism: pilot project for Lahore, 2021, passed and completed.
  3. Hassan Shakil, BSCS, Context based movie recommendation system, 2020, passed and completed.
  4. Hashir Murtaza, BSCS, No parking and wrong way traffic detection, 2020, passed and completed.
  5. Noman Afzal, BSCS, Visual landmark detection and recognition, 2020, passed and completed.
  6. Laiqa Imran, BSCS, Look at my hand, sign language translation, 2020, passed and completed.
  7. Azeem Ashraf, BSCS, Hardy: Smart office boy, autonomous robot, 2019, passed and completed.
  8. Junaid Javaid, BSCS, Decentralized peer-to-peer ledger for academic records, 2019, passed and completed.
  9. Osama Khalid, BSCS, Learning based automated human tracking system, 2018, passed and completed.
  10. Muhammad Tahoor, BSCS, Estimation of fracture healing in long bones using acoustic signals, 2017, passed and completed.

 Conference papers:

  1. Qureshi, A. N. & Schetinin, V. (2013). Helping clinicians to read brain scans. University of Bedfordshire Research Conference 2013, Bedfordshire, UK.
  2. Qureshi, A. N. & Schetinin, V. (2014). Computer-aided segmentation and estimation of indices in brain CT scans. Medical Image Understanding and Analysis 2014, City University London, pp. 161-166.
  3. Soltaninejad, M., Lambrou, T., Qureshi, A. N., Allinson, N. & Ye, X. (2014). A hybrid method for haemorrhage segmentation in trauma brain CT. Medical Image Understanding and Analysis 2014, City University London, pp. 99-104.
  4. Qureshi, A. N. (2014). A framework for segmentation and estimation of intracranial measurements in CT scans. In Biomedical Engineering Conference (CIBEC), 2014 Cairo International, 129-132, IEEE.
  5. Hassan, M. M., Qureshi, A. N., Moreno, A., & Tukiainen, M. (2017). Participatory Refinement of Participatory Outcomes: Students Iterating over the Design of an Interactive Mobile Learning Application. In Learning and Teaching in Computing and Engineering (LaTICE), 2017 International Conference on, 76-81, IEEE.
  6. Hassan, M.M. & Qureshi, A. N. (2017). Situating Adaptive Educational Hypermedia into the Local Context of Developing Nations, In Proceedings of 2nd International Conference on Communication and Information Systems (ICCIS 2017). ACM, New York, NY, USA, pp. 376-381.
  7. Hassan, M.M. & Qureshi, A. N. (2018). Disrupting the Rote Learning Loop: CS Majors Iterating over Learning Modules with an Adaptive Educational Hypermedia, 14th Intelligent Tutoring Systems (ITS) Conference, Montreal, Canada, [978-3-319-91463-3, ITS 2018, Volume 10858 of the Lecture Notes in Computer Science, Chapter 8].
  8. Lodhi, A. M., Qureshi, A. N., Sharif, U. & Zahid, M. (2018). A Novel Approach using Voting from ECG Leads to Detect Myocardial Infarction, Intelligent Systems Conference (IntelliSys) 2018 IEEE, London, UK, pp. 884-890, [978-3-030-01056-0, IntelliSys 2018, AISC 869, Chapter 27].
  9. Iqbal, S., Qureshi, A. N. & Lodhi, A. M., (2018). Content Based Video retrieval using Convolutional Neural Network, Intelligent Systems Conference (IntelliSys) 2018 IEEE, London, UK, pp. 108-115, [978-3-030-01053-9, IntelliSys 2018, AISC 868, Chapter 12].
  10. Hassan, M. M., Qureshi, A. N., Moreno, A., & Tukiainen, M. (2018). Smart Learning Analytics and Frequent Formative Assessments to Improve Student Retention. In International Conference on Smart Communications and Networking (SmartNets) IEEE, Tunisia, ISBN: 978-1-5386-9202-8, pp. 1-6.
  11. Mahmood, A., Azzuhri, S. R., Kiah, L. B. & Qureshi, A. N. (2018). Wireless Backhaul Network Optimization using automated KPIs monitoring system based on Time Series Forecasting, The World Symposium on Communication Engineering (WSCE) 2018, Singapore, IEEE, ISBN: 978-1-5386-7985-2, pp. 27-32.
  12. Hassan, M. M., Tukiainen, M. & Qureshi, A. N., (2019). (Un) Discounted Usability: Evaluating Low-Budget Educational Technology Project with Dual-Personae Evaluators. In 8th International Conference on Software and Information Engineering, Egypt, ACM, ISBN: 978-1-4503-6105-7, pp. 253-258.
  13. Atteeq M, Khan M. F. & Qureshi A. N. (2019). Foetus Heart Beat Extraction from Mother’s PCG Using Blind Source Separation, 11th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2019), Stockholm, Sweden, ACM, ISBN: 978-1-4503-6231-3/19/05.
  14. Khan M. F., Atteeq M & Qureshi A. N. (2019). Computer Aided Detection of Normal and Abnormal Heart Sound Using PCG, 11th International Conference on Bioinformatics and Biomedical Technology (ICBBT 2019), Stockholm, Sweden, ACM, ISBN: 978-1-4503-6231-3/19/05.
  15. Hassan, M. M., Tukiainen, M., & Qureshi, A. N. (2019, December). Participatory Heuristic Evaluations of Jeliot Mobile: End-users evaluating usability of their mlearning application. In 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) (pp. 1-6). IEEE.
  16. Iqbal S., Qureshi A. N., Akter M. (2020) Using Local Binary Patterns and Convolutional Neural Networks for Melanoma Detection. In: Bi Y., Bhatia R., Kapoor S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038, pp 782-789, Springer, Cham.
  17. Sharif U., Qureshi A.N., Afza S. (2021) ORTIA: An Algorithm to Improve Quality of Experience in HTTP Adaptive Bitrate Streaming Sessions. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications (IntelliSys) 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham.
  18. Mahmood, A., Azzuhri, S. R., Kiah, L. B., Qureshi, A. N., Jaan P., & Sadia I. (2022) Hand-foot-mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data, in IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2022), Kota Kinabalu, Sabah, Malaysia.
  19. Iqbal, S., Qureshi, A. N., Aurangzeb, K. and Javeed, K., (2023) Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data, 5th International Conference on Bio-engineering for Smart Technologies (BioSMART), Paris, France, pp. 1-4.

Funded Projects and Patents

Funded Project – Hypermedia based Learning: hl.ucp.edu.pk

Funded (£40,000) in 2017 by The Punjab Group, Lahore, Pakistan.

Role – Project Director and Principal Investigator of the classification and clustering algorithms and their implementation

Patent (Registration # 47092-Copr) granted in December, 2022.

The challenges faced by the L2 learners of English language and mathematics for freshmen are minimised using an adaptive and adaptable Hypermedia based automated system which uses machine learning to classify the demographics, cognitive, learning and knowledge profiles and performance of students to present them with personalized, hypermedia content. The outcome is individualized learning experience for the students based on their specific traits which minimizes the first and second order barriers of teaching in conventional classroom environment.

Publications

Articles in peer-reviewed Journals:

  1. Maple, C., Prakash, E., Huang, W., & Qureshi, A. N. (2014). Taxonomy of optimisation techniques and applications. International Journal of Computer Applications in Technology, 49(3):251-262, DOI: https://doi.org/10.1504/IJCAT.2014.062361
  2. Qureshi, A. N. (2015). Semi-automated Classification of CT Scans in Traumatic Brain Injury Patients. International Journal of Computer Applications, 113(9):1-8.
  3. Lodhi, A. M., Qureshi, A. N., Sharif, U. & Zahid, M. (2018). Detection of Myocardial Infarction in ECG Base Leads using Deep Convolutional Neural Networks, KIET Journal of Computing & Information Sciences (ICCIS-2018), PAF-Karachi Institute of Economics & Technology (PAF-KIET), Pakistan.
  4. Mahmood, A., Kiah L. B., Z’aba, M. R., Qureshi, A. N., Kassim, M., Hasan, A. , Zati Hakim, Z., Jagadeesh, K., Iraj, S. A. &  Azzuhri, S. R. (2020). Capacity and Frequency Optimization of Wireless Backhaul Network using Traffic Forecasting, IEEE Access (IF=4.098), pp. 23264 – 23276.
  5. Mahmood, A., Kiah, M.L.M., Azzuhri, S.R., Kamal M.M., Eldabi T., Qureshi A.N., Azizul Z. & Z’aba M. R. (2020). Wireless backhaul network’s capacity optimization using time series forecasting approach. J Ambient Intelligence & Human Computing (IF=4.59).
  6. Ali, K., Quershi, A. N., Arifin, A. A. B., Bhatti, M. S., Sohail, A., & Hassan, R. (2022). Deep Image Restoration Model: A Defense Method Against Adversarial Attacks. CMC-Computers, Materials & Continua, 71(2), 2209-2224.
  7. Ali, K., Quershi, A. N. (2022). Restoration of Adversarial Examples Using Image Arithmetic Operations. Intelligent Automation & Soft Computing, 32(1), 271–284.
  8. Iqbal, S., Qureshi, A. N., Mustafa, G. (2022). Hybridization of CNN with LBP for Classification of Melanoma Images. CMC-Computers, Materials & Continua, 71(3), 4915–4939.
  9. Arshad, I., Qureshi, A. N. (2022). Detection of Lung Nodules on X-Ray images using transfer learning and hand-crafted features. CMC-Computers, Materials & Continua 72(1), 1445-1463.
  10. Iqbal, S., Qureshi, A. N. (2022). Deep-Hist: Breast cancer diagnosis through histopathological images using convolution neural network. Journal of Intelligent & Fuzzy Systems, 43(1), 1347-1364.
  11. Ali, K., Qureshi, A. N. (2022). Defending Adversarial Examples by a Clipped Residual U-Net Model. Intelligent Automation & Soft Computing, 35(2), 2237-2256.
  12. Iqbal, S., Qureshi, A. N. (2022). A heteromorphous deep CNN framework for Medical Image Segmentation using Local Binary Pattern. IEEE Access, 10(1), 63466-63480.
  13. Ali, K., Qureshi, A. N. (2022). Denoising brain MRI images by mixing concatenation and residual learning (MCR), Computer Systems Science and Engineering, 45, no.2, pp. 1167–1186, 2023.
  14. Iqbal, S., Qureshi, A. N., Ullah, A., Li, J., Mahmood, T. (2022). Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning. Applied Sciences, 11870, MDPI.
  15. Iqbal, S., Qureshi, A. N., Ullah, A., Li, J., Mahmood, T. (2023). On the Analyses of Medical Images using Traditional Machine Learning Techniques and Convolutional Neural Networks. Archives of Computational Methods in Engineering.
  16. Iqbal, S., Qureshi, A. N., Li, J., Arshad, I., Mahmood, T. (2023). Dynamic learning for imbalanced data in learning chest X-ray and CT images. Heliyon.
  17. Iqbal, S., Qureshi, A. N., (2023). AMIAC: Adaptive Medical Image Analyses and Classification, a Robust Self-Learning Framework, Neural Computing and Applications, Springer (accepted, July 2023).