Rasheed G. Jimoh

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: jimoh_rasheed@yahoo.com


Research Interests: Autonomic Computing, Information Security, Network Security, Computing Platform, Information-Theoretic Security


Jimoh Rasheed Gbenga is currently an Acting Dean of Faculty of Communication and Information Science (FCIS), University of Ilorin, Nigeria. He attended Universiti Utara Malaysia, Malaysia where he got Ph.D. in Information Technology. His research interests are: Information Security, Soft Computing and Machine Learning. Professional Membership: A member of Computer Professionals[Registration Council of Nigeria]-CPN A member of Nigeria Computer Society of Nigeria (NCS). A member of IEEE Nigeria Chapter.

Author Articles
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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