Maryam Shuaib

Work place: Department of Cyber Security, Federal University of Technology, Minna, Nigeria



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Database Management System, Data Structures and Algorithms


Maryam Shuaib is a Postgraduate student in the Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria. She has a B.Tech. degree in Mathematics with Computer Science. She was the Special Assistant to the Governor of Niger State, Nigeria on ICT Development between 2012-2015. Her research interests include cybersecurity, IoT and Database Security. Maryam is an Oracle Database 11g Certified Associate and a member of the Association of Nigerian Authors.

Author Articles
Comparative Analysis of Classification Algorithms for Email Spam Detection

By Shafii Muhammad Abdulhamid Maryam Shuaib Oluwafemi Osho Idris Ismaila John K. Alhassan

DOI:, Pub. Date: 8 Jan. 2018

The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Na?ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Na?ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms’ performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

[...] Read more.
Other Articles