Muhammad Haroonb

Work place: Department of Computing & Information Technology University of Gujrat Lahore Sub Campus, Lahore, Pakistan



Research Interests: Data Structures, Data Structures and Algorithms, Data Mining, Computational Learning Theory


Muhammad Haroon is working as Associate Lecturer at University of Gujrat Lahore Sub Campus and student of MS Computer Science with the specialization of Database and Data Mining in Virtual University of Pakistan. His areas of interest are Database Development, Data Mining and Data Processing.

Author Articles
Evaluation Study of Software Quality Management ‎‎(SQM) and Quantitative Process Management ‎‎(QPM) in Pakistan Software Houses

By Muhammad Haroonb

DOI:, Pub. Date: 8 Aug. 2020

Key Process Areas (KPAs) for ‎Software Engineering Institute (SEI) Maturity ‎Level 4 can be described in terms of Quantitative ‎Process Management (QPM) which is the metric ‎to control the quantitative performance of a ‎software project. On the other hand, Software ‎Quality Management (SQM) monitors and ‎controls the quality of the project. The survey ‎conducted in this paper covers around 20 ‎software houses of Pakistan. The study revealed ‎that there is weakness in both KPAs, SQM and ‎QPM. Each KPA defines a set of rules that are necessary to be followed to meet the standard but many organizations fail to follow these rules defined in every KPA. If specified and ‎appropriate measures are taken, the software ‎industry will lift it up to the higher CMMI Level.‎

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Comparative Analysis of Stemming Algorithms for Web Text Mining

By Muhammad Haroonb

DOI:, Pub. Date: 8 Sep. 2018

As the massive data is increasing exponentially on web and information retrieval systems and the data retrieval has now become challenging. Stemming is used to produce meaningful terms by stemming characters which finally result in accurate and most relevant results. The core purpose of stemming algorithm is to get useful terms and to reduce grammatical forms in morphological structure of some language. This paper describes the different types of stemming algorithms which work differently in different types of corpus and explains the comparative study of stemming algorithms on the basis of stem production, efficiency and effectiveness in information retrieval systems.

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Other Articles