1. Qadri, S., D.M. Khan, S.F. Qadri, A. Razzaq and N. Ahmad et al., 2017. Multisource data fusion framework for land use/land cover classification using machine vision. J. Sensors, 2017: 8-8.
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2. Qadri, S., D.M. Khan, F. Ahmad, S.F. Qadri, M. UL. Rehman, S. Muhammad and M. Mutiullah, 2017. A novel optimized land cover classification framework using data mining techniques. Sindh Univ. Res. J., 49: 433-440.
3. Mushtaq, M.F., D.M. Khan, U. Akram, S. Ullah and A.Tariq, 2017. A cognitive architecture for self learning in humanoid robots. Int. J. Compu. tSc.i Network Secur., 17: 26-36.
4. Ullah, S., D.M. Khan, A. ur Rehman, M.A. Siddique and A. Khan, 2016. An effective modeling to minimize the transmission time in vanet by reducing routing overhead. Sci. Int. Lahore, 28: 3041-3047.
5. Sajjad, S., D.M. Khan, N. Saher and F. Shahzad, 2016. The usability analysis of mobile interfaces. Sci. Int. Lahore, 28: 1097-1103.
6. Qadri, S., D.M. Khan, F. Ahmad, S.F. Qadri and M.E. Babar et al., 2016. A comparative study of land cover classification by using multispectral and texture data. Bio. Med. Res. Int., 2016: 12-12.
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7. Karim A., N. Khan, D.M. Khan, N. Saher and F. Shahzad, 2016. The application of the subdivision algorithm for surface modeling. Sci. Int., 28: 1071-1077.
8. Fayyaz, Y., D.M. Khan, F. Fayyaz, S. Qadri, S. Naweed and M. Fahad, 2016. The evaluation of voice-over internet protocol (voip) by means of trixbox. Int. J. Nat. Eng. Sci., 9: 45-53.
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9. Yaqoob, I., D.M. Khan, N. Saher, F. Shahzad and S. Qadri, 2015. An innovative theoretical model for humanoid robots. Int. J.Nat. Eng., 9: 28-38.
10. Saher,N., D.M. Khan and F. Shahzad, 2015. The quality assessment of software testing procedure and its effects. Sci. Int. Lahore, 27: 4865-4874.
11. Saher, N., D.M. Khan, F. Shahzad and S. Qadri, 2015. The impact of employees’ turnover at the productivity of a software. Int. J. Nat. Eng. Sci., 9: 23-27.
12. Qadri, S., D.M. Khan, M. ul-Rehman, M. Fahad, M. Shahid, Mutiullah and E.A. Rehmani, 2015. Identification of buildings and roads area in an aerial image by applying morphological filters. Int. J. Nat. Eng. Sci., 9: 34-37.
13. Maqbool, I., S. Qadri, D.M. Khan and M. Fahad, 2015. Identification of mango leaves by using artificial intelligence. Int. J. Nat. Eng. Sci., 9: 45-53.
14. Khan, D.M., F. Shahzad, N. Saher and N.Mohamudally, 2015. A relative study of load balancing and management techniques in a distributed system: the iub case study. Sci. Int., 27: 3171-3177.
15. Khan, D.M. and N. Mohamudally, 2015. Data Mining: A Unified Approach. Scholars Press, USA, ISBN: 978-3639769692.
16. Saeed, F., D.M. Khan, N. Saher, F. Shahzad and N. Ammer, 2014. A novel framework for interactive mobile applications. Sci. Int., 26: 2089-2095.
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17. Khan, D.M., F. Shahzad, N. Saher and N. Mohamudally, 2014. Optimization and analysis of clusters. Sci. Int., 26: 1959-1971.
18. Khan, D.M. and N. Mohamudally, 2014. The relevancy of a unified data mining theory (udmt) for the big data. J. Appl. Environ. Biol. Sci., 4: 160-165.
19. Khan, D.M., N. Mohamudally and D.K.R. Babajee, 2013. A unified theoretical framework for data mining. Procedia Comput. Sci., 17: 104-113.
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20. Saher, N., D. M. Khan, F. Shahzad and N. Mohamudally, 2012. The prospects of erp systems on quality of education andresearch in higher education and research institutions of Pakistan. J. Comput., 4: 115-119.
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21. Khan, D.M., N. Saher and F. Shahzad and N. Mohamudally, 2012. The human resource development (hrd) at the highereducation and research institutions of pakistan: The iubcase study. J. Comput., 4: 120-124.
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22. Khan, D.M., N. Mohamudally and D.KR. Babajee, 2012. The formulation of a data mining theory for the knowledge extraction by means of a multiagent system. J. Comput., 4: 29-38.
23. Khan, D.M., N. Mohamudally and D.K.R. Babajee, 2012. Towards the Formulation of a Unified Data Mining Theory, Implemented by means of Multiagent Systems (MASs). In: Advances in Data Mining Knowledge Discovery and Applications, Karahoca, A. (Ed.)., Under CC BY 3.0 license, USA, ISBN: 978535107484 Pages: 3-42.
24. Khan, D.M., N. Mohamudally and D.K.R. Babajee, 2012. Investigating the statistical linear relation between the model selection criterion and the complexities of data mining algorithms. J. Comput., 4: 14-28.
25. Khan, D.M., N. Saher, F. Shahzad and N. Mohamudally, 2012. The integration of networking and computerization towards e-education and e-learning at the higher education and research institutions of Pakistan. Int. J. Comput. Sci., 9: 546-551.
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26. Khan, D.M. and N. Mohamudally, 2012. The adaptability of conventional data mining algorithms through intelligent mobile agents in modern distributed systems. Int. J. Comput. Sci. Issues, 9: 38-46.
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27. Khan, D.M. and N. Mohamudally, 2012. Model selection criterions as data mining algorithms’ selector the selection of data mining algorithms through model selection criterions. J. Comput., 4: 102-114.
28. Khan, D.M. and N. Mohamudally, 2012. Intelligent versus malicious agent: A comparative study. Int. J. Comput. Sci., 9: 605-611.
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29. Khan, D.M. and N. Mohamudally, 2012. A comparative study of single-step and multi-step data mining tools. J. Comput., 4: 26-41.
30. Khalid, A., Y. Fayyaz and D.M. Khan, 2012. Cloud computing: Deployment issues for the enterprise systems. J. Comput., 4: 61-65.
31. Khan, D.M. and N. Mohamudally, 2011. From mainframe to cloud computing: A study of programming paradigms with the evolution of clientserver architecture. J. Comput., 4: 21-27.
32. Khan, D.M. and N. Mohamudally, 2011. An integration of k-means and decision tree (id3) towards a more efficient data mining algorithm. J. Comput., 3: 76-82.