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Item Modeling students’ academic performance using artificial neural network(Federal University, Ndufu-Alike Ikwo (FUNAI), Nigeria, 2016) Asogwa, O. C.; Udomboso, C. G.Artificial Neural Network has been discovered as a better alternative to traditional models and that is why a model based on the Multilayer Perceptron algorithm was developed in this study. The appropriate number of hidden neurons that best modeled the academic performance of students was determined by the developed Network algorithm. Test data evaluation showed that Network Architecture 17-80 -1 was chosen among the numerous developed network architectures because of its model performances. The chosen network architecture gave the minimum value of Mean Square Error (MSE = 0.0718), minimum value of Network Information Criteria (NIC = 0.0743), maximum value of R- Square (R2=0.8975) and maximum value of Adjusted Network Information Criteria (ANIC= 0.8931). It was equally observed that there were patterns in the movement of hidden neurons against the model evaluation criteria. As the number of the hidden neurons appreciates the value of both MSE and NIC decreases down the plot, while that of ^-Square and ^MCvalues appreciate down the plot. The network was able to model the research problem with acceptable values judging from the model checking criteria considered in this work. Also the order of contribution of the predictor variables to the model was determined.Item Statistical neural network modelling of cholera in Nigeria and South Africa with implications for psychosocial support(Taylor & Francis, 2017) Oduaran, C.; Udomboso, C. G.Cholera has been studied from different perspectives since its first outbreak in the 16th century. However, little is known about the psychosocial support needed, which becomes critical because its eradication has continued to defy attempts by many governments. This paper is based largely on data obtained from World Health Organization annual observatory website for Nigeria and South Africa. Cases of missing observations were estimated using spline interpolation. Statistical neural network was used to estimate the fatality rate, and forecasts were made for 2030. Results showed that fatality rates were decreasing in both countries, with a faster rate in Nigeria (-0.04) compared to South Africa (-0.06). However, the disease would still not have been eradicated by 2030. This calls for stronger concerted efforts by the government and international community in combating the disease in Africa. One major intervention would be the application of targeted psychosocial support that victims, friends, families and communities of victims lack at the moment.