Mahmood K. Pathan

Work place: Federal Urdu University of Arts, Science & Technology, Karachi, 75300, Pakistan



Research Interests: Data Mining, Data Structures and Algorithms


Mahmood Khan Pathan received his M.Sc. degree in Pure Mathematics in 1974 from the University of Karachi and Ph. D. degree in Applied Algebra from Brunel, The University of West London, United Kingdom in 1992. He served NED University of Engineering & Technology, Karachi-Pakistan as Dean of Faculty of Information, Sciences & Technology from 2005 to 2013. His research interests include finite fields, cryptography and Educational Data Mining.

Author Articles
Predicting Student Academic Performance at Degree Level: A Case Study

By Raheela Asif Agathe Merceron Mahmood K. Pathan

DOI:, Pub. Date: 8 Dec. 2014

Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.

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Margin Based Learning: A Framework for Acoustic Model Parameter Estimation

By Syed Abbas Ali Najmi Ghani Haider Mahmood K. Pathan

DOI:, Pub. Date: 8 Nov. 2012

Statistical learning theory has been introduced in the field of machine learning since last three decades. In speech recognition application, SLT combines generalization function and empirical risk in single margin based objective function for optimization. This paper incorporated separation (misclassification) measures conforming to conventional discriminative training criterion in loss function definition of margin based method to derive the mathematical framework for acoustic model parameter estimation and discuss some important issues related to hinge loss function of the derived model to enhance the performance of speech recognition system.

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