John Kolo Alhassan

Work place: Department of Computer Science, Federal University of Technology Minna, Nigeria



Research Interests: Data Mining, Computer systems and computational processes, Computer Science & Information Technology


Dr. J. K. Alhassan obtained B.Tech (Mathematics/ Computer Science) at the Federal University of Technology, Minna. M.Sc (Computer Science) at University of Ibadan, Nigeria in 2006, and PhD (Computer Science), at Federal University of Technology, Minna, Nigeria. He carried out part of his PhD research at United Institute of Informatics Problems, National Academy of Sciences of Belarus (UIIP NASB) Minsk, Republic of Belarus. He is currently the Acting Head, at the Department of Cyber Security Science, Federal University of Technology, Minna, Niger State, Nigeria. His research interest includes Artificial Intelligence, Data Mining, Internet Technology, Database Management System, Software Architecture, Machine Learning, Human Computer Interaction and Computer Security. He is a member of Computer Professionals (Registration Council) of Nigeria (CPN).

Author Articles
Enhancing Fast Fourier Transform Algorithm for Keystroke Acoustic Emanation Denoising Strategy on Real-Time Scenario

By Suleiman Ahmad John Kolo Alhassan Shafii Muhammad Abdulhamid Suleiman Zubairu

DOI:, Pub. Date: 8 Feb. 2024

The use of virtual keyboards in mobile devices such as smartphones and tablets has become an essential tool for inputting information. The sound of keystrokes has been observed in previous studies to be recorded along with ambient noises, such as those produced by uncontrolled student noise, fans, doors and windows, moving cars, and similar sources. The presence of such noises negatively affects the quality of the keystrokes signal, which in turn affects keystroke analysis. The traditional FFT-based denoising methods are vital but they are often limited by their inability to adapt to the varying characteristics of real-world audio and noises. This paper proposes an enhanced Fast Fourier Transform (FFT) with an adaptive threshold technique that reduces ambient noises. The adaptive threshold technique is developed to identify frequency bins that contain noise and set their sizes to zero or attenuate them to reduce the noise. The paper evaluates the performance of the enhanced FFT with adaptive threshold on keystrokes recorded audio and validates it through extensive experimentation. The results show that the enhanced FFT outperforms the traditional FFT in terms of speed and the amount of noise removed from the recorded audio signal, indicating a significant improvement.

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A Decision Tree Approach for Predicting Students Academic Performance

By Kolo David Kolo Solomon A. Adepoju John Kolo Alhassan

DOI:, Pub. Date: 8 Oct. 2015

This research is on the use of a decision tree approach for predicting students' academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.

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