Muazzam A. Siddiqui

Work place: Department of Information System, King Abdulaziz University, Jeddah, Saudi Arabia



Research Interests: Data Mining, Machine Learning, Computer systems and computational processes, Text Mining, Information Retrieval


Muazzam Ahmed Siddiqui is an assistant professor at the Faculty of Computing and Information Technology, King Abdulaziz University. He received his BE in electrical engineering from NED University of Engineering and Technology, Pakistan, and MS in computer science and PhD in modeling and simulation from University of Central Florida. His research interests include text mining, information extraction, data mining and machine learning.

Author Articles
Mining Wikipedia to Rank Rock Guitarists

By Muazzam A. Siddiqui

DOI:, Pub. Date: 8 Nov. 2015

We present a method to find the most influential rock guitarist by applying Google PageRank algorithm to information extracted from Wikipedia articles. The influence of a guitarist was estimated by the number of guitarists citing him/her as an influence and the influence of the latter. We extracted this who-influenced-whom data from the Wikipedia biographies and converted them to a directed graph where a node represented a guitarist and an edge between two nodes indicated the influence of one guitarist over the other. Next we used Google PageRank algorithm to rank the guitarists. The results are most interesting and provide a quantitative foundation to the idea that most of the contemporary rock guitarists are influenced by early blues guitarists. Although no direct comparison exist, the list was still validated against a number of other best-of lists available online and found to be mostly compatible.

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Query Optimization in Arabic Plagiarism Detection: An Empirical Study

By Imtiaz Hussain Khan Muazzam A. Siddiqui Kamal M. Jambi Muhammad Imran Abobakr A. Bagais

DOI:, Pub. Date: 8 Dec. 2014

This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and f-measure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.

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