Opeyemi A. Abisoye

Work place: Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, P.M.B. 65, Minna, Niger State, Nigeria

E-mail: opeglo@yahoo.com.au


Research Interests: Computer systems and computational processes, Computational Learning Theory, Data Mining, Data Structures and Algorithms


Abisoye Opeyemi A. was born in Ogbomoso, Oyo State, Nigeria.. She attended University of Ilorin, Ilorin, Nigeria where she obtained her BSc, Msc, degree in Computer Science. She is currently a PhD. Student of the same institution. She is major in Computational Intelligence, Machine Learning, Data Mining, and Soft Computing. She serves as a Lecturer I, in the Department of Computer Science, SICT, Federal University of Technology, Minna, Niger State, Nigeria from May 23rd 2007 Till Date.

Author Articles
Simulation of Electric Power Plant Performance Using Excel®-VBA

By Blessing O. Abisoye Opeyemi A. Abisoye

DOI: https://doi.org/10.5815/ijieeb.2018.03.02, Pub. Date: 8 May 2018

This paper presents the failure and repair simulation for electric power house with N Turbo-alternator, where N may be up to 32. The program employs a pseudo-random number generator for individual power plant that can be described by exponential probability density functions. The resulting sequences of failure and repair events are then combined for the plants to give scenarios for different time horizons. The implementation in Excel®-VBA includes an appropriately designed userform containing the macro Active-X control for input of relevant information. The result shows that as the number of samples increased the behavior of the random events better represented the desired form with a correlation of almost 99% for 25 trials. This corresponds to a confidence interval of better than 95% and hence should be used as the median for practical applications. The results were tested and the distributions of the events were found to be close approximation of the target exponential distributions.

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Comparative Study on the Prediction of Symptomatic and Climatic based Malaria Parasite Counts Using Machine Learning Models

By Opeyemi A. Abisoye Rasheed G. Jimoh

DOI: https://doi.org/10.5815/ijmecs.2018.04.03, Pub. Date: 8 Apr. 2018

Dynamics of Malaria parasite diagnosis is complex and been widely studied. Research is on-going on the effects of climatic variations on symptomatic malaria infection. Malaria diagnosis can be asymptomatically or symptomatically low, mild and high. An analytical program is needed to detect individual malaria parasite counts from complex network of several infection counts. This study adopted the experimental malaria parasite counts collected from selected hospitals in Minna Metropolis, Niger State, Nigeria and Climatic data collected at the time the experiment was conducted from NECOP, Bosso, FUT Minna, Niger State, Nigeria. One thousand and two hundred (1,200) experimental data were collected and two classifiers Support Vector Machine (SVM), Artificial Neural Network (ANN) do the prediction. Experimental results indicated that SVM produced Accuracy 85.60%, Sensitivity 84.06%, Specificity 86.49%, False Positive Rate(FPr) 0.1351% and False Negative Rate(FNr) 0.1594% than Neural Network model of Accuracy 48.33%, Sensitivity 60.61%, Specificity 45.48%, low False Positive Rate (FPr) 0.5442% and False Negative Rate(FNr) 0.3939% as depicted in their respective confusion matrix.

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