Mohammed O. Yahaya

Work place: Computer Science and Engineering Department, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia



Research Interests: Computational Game Theory, Computational Learning Theory, Computer Networks, Application Security, Analysis of Algorithms, Models of Computation


Mohammed Onimisi Yahaya is currently an assistant professor at the College of Computer Science and Engineering, University of Hafr Al Batin. He received the Ph.D. degree in computer science and engineering from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia in 2014.
Dr Mohammed's research interests include game theory application in modelling interactions behavior in peer-to-peer networks, network functions virtualization and application of machine learning in various disciplines.

Author Articles
Detecting Anomalies in Students‟ Results Using Decision Trees

By Hamza O. Salami Ruqayyah S. Ibrahim Mohammed O. Yahaya

DOI:, Pub. Date: 8 Jul. 2016

Examinations are one of the most important activities that take place in institutions of learning. In many Nigerian universities, series of meetings are held to manually examine and approve computed student examination results. During such meetings, students‟ results are scrutinized. Reasonable explanations must be provided for any anomaly that is discovered in a result before the result is approved. This result approval process is prone to some challenges such as fatigue arising from the long duration of the meetings and wastage of man-hours that could have been used for other productive tasks. The aim of this work is to build decision tree models for automatically detecting anomalies in students‟ examination results. The Waikato Environment for Knowledge Analysis (WEKA) data mining workbench was used to build decision tree models, which generated interesting rules for each anomaly. Results of the study yielded high performances when evaluated using accuracy, sensitivity and specificity. Moreover, a Windows-based anomaly detection tool was built which incorporated the decision tree rules.

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