Name: Jamal Uddin

Country: Pakistan


Assistant Professor,
Computer Science Department,
Qurtuba University Of Science & Information Technology,
Peshawar, KP,

Email: Send an Email


Moh Haya Khan,
Qafila Road,
Tehkal Payan,

Research Interests:

  • Utilizing strengths of proposed and classical data mining & artificial intelligence techniques in several applications like for example in software engineering, information retrieval system,
  • and image processing is my key research area.
  • Having a strong mathematical background I have keen interest in improving the mathematical basis for different data mining, artificial intelligence techniques and knowledge
  • discovery processes.
  • I am also interested in exploration of strength and weaknesses of evaluation measures to well judge data mining & artificial intelligence techniques.
  • Taking the benefits of the strengths of rough set theory as better alternative for data analysis, soft computing and knowledge discovery tasks like prediction, optimization, clustering and classification.



  • 5 Stars Publication Award in PhD
  • 5 Stars Graduate on time Award in PhD
  • Official Mendeley Advisor
  • Certified Tutor of LaTeX
  • Reviewer of different journals and conferences
  • Bronze Medal (3rd Position) in Research & Innovation competetion’2016, UTHM.
  • Participated in 3MIN Thesis Research competetion’2016, UTHM. 


  1. Uddin, J., Ghazali, R., Deris, MM., (2017), An Empirical Analysis of Rough Set Categorical Clustering Techniques , PLoS ONE, PP 1-22, Vol 12/1, DOI: 10.1371/journal.pone.0164803 (ISI Q1, IF=3.54)
  2. Uddin, J., Ghazali, R., Deris, MM., Naseem, R.and Shah, H. (2016), A Survey on Bug Prioritization, Artificial Intelligence Review, Springer, PP 1-36, DOI: 10.1007/s10462-016-9478-6 (ISI Q2, IF=2.1)
  3. Uddin, J., Ghazali, R., Deris, MM., (2016), Does Number of Clusters Affects the Purity and Entropy of Clustering?, International Conference on Soft Computing and Data Mining, Vol 549, ISBN : 978- 3-319-51279-2, Springer Conference.
  4. Uddin, J., Ghazali, R., Deris, MM, Abawajy, J. and Naseem, R. (2017), A rough value set categorical clustering technique for supply base management, Industrial Marketing Management, Elsevier, Under Review.
  5. Uddin, J., Ghazali, R., Deris, MM., and Naseem, R. (2016), Information-theoretic rough purity measure for clustering categorical data with uncertain attribute values, International Journal of Information Technology & Decision Making, World Scientific. Under Review.
  6. Uddin, J. and Mohyuddin, S.T. (2012), Modified Variation of Parameters Method for System of PDEs, International Journal of Modern Applied Physics, PP. 201-31, 1(1), ISSN:2168-1139, Modern Scientific Press.
  7. Abdi, ANE., Mohamad, K., Hasheem, Y., Naseem, R., Uddin, J., and Aamir, M., (2016), Corrupted MP4 Carving Using MP4-Karver, International Journal of Advanced Computer Science and Applications, 7(3), pages 88–93.
  8. Salleh, MN., Hussain, K., Naseem, R., and Uddin, J., (2016), Optimization of ANFIS using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs, The Second International Conference on Soft Computing and Data Mining, Vol 549, ISBN : 978-3-319 51279-2, Springer Conference.
  9. Nawi, N., Rehman, M., Hamid, N., Khan, A., Naseem R., and Uddin, J., (2016),Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm, The Second International Conference on Soft Computing and Data Mining, Vol 549, ISBN : 978-3-319-51279-2, Springer Conference.
  10. Naseem, R., Deris, MM., Maqbool,O. and Uddin, J., Hierarchical clusterer combinations for software modularization, The International Journal of Software Engineering and Knowledge Engineering, Under Review.