Browsing by Author "James, T. O."

Now showing 1 - 4 of 4

ARIMA model and neural network: a comparative study of crime rate modelling
(2015) James, T. O.; Suleiman, S.; Udomboso, C. G.; Babayemi, A. W.
Crime rate is a serious issue that affects everyone in society. It affects the victims, perpetrators, their families the government and even reality of good governance. In this study forecasting of crime rate using autoregression integrated moving average (AR1MA) model was compared with feed forward neural networks. The J multi software was used for analysis of data gotten from State Police Headquarter in Kebbi State from January 2004 to December 2013 and the series was stationary at first difference and ARIMA (0, 1, 1) was obtained as the best model for the series. This was model by Neural Network using SPSS. In the training of the network, the samples were automatically partitioned in to 73.3% of training and 26.7% of testing. The computational result shows that Artificial Neural Network provides better model than ARIMA by having minimum error in the in-sample and out -of- sample in MAE, MSE, and RMSE with 3.84614, 2.00466 and 1.41586 respectively.
Autoregressive distributed lags (ARDL) modelling of the impacts of climate change on rice production in Kebbi State
(Professional Statisticians Society of Nigeria, 2018) James, T. O.; Babayemi, A. W.; Abdulmuahymin, A. S.; Udomboso, C. G.; Bello, M. L.
Autoregress Distributed Lag (ARDL) is an econometric model that determines the long run a d short run association between the Serial (Stationary/ non-stationary as well as reparameterizing them to Error correction model (EMC). Rice cultivation and production is a major source of income for millions of households around the globe especially in Nigeria. It is also a major staple food, but Climate change poses great threat to the stability and sustainability of rice production for sufficient agricultural system, since most Nigeria consumes rice more than other foods and Kebbi state, is one of the major states contributing to the total rice output of the country. Climate change is the major challenge facing rice production. This study therefore, investigates the long-run and short run effect of factors affecting rice production in Kebbi State. 1000 simulations of data were obtained from the data collected between the period of 2005 to 2016 from the state Ministry of Agriculture. The result showed that rainfall has impact both in the long run and short run; 100% increase in rainfall, will tend to give 99.98% increase in rice production in the long-run. However, temperature tends to show insignificant impact on rice production. The result of this paper facilitate understanding for government and agriculturist in the linkages between climate change variables and rice production which can boost and increases the production of rice in Kebbi State.
On R2 contribution and statistical inference of the change in the hidden and input units of the statistical neural networks
(Society of African Journal Editors, 2012-11) Udomboso, C. G.; James, T. O.; Odim, M. O.
Determining the number of liitltlen units for obtaining optimal network performance has been a concern over the years ilespite empirical results showing that with higher neurons, the netivork error is retlucetl. This has led to indiscrimate increase in the hidden neurons, thereby bringing about overfitting. On the other hand, using too few hidden neurons leads to error bias, which can make neural network statistically unfit. In this paper, we developed a model for R1 for investigating changes in hidden and input units, as well as developed tests that can be used in determining the number of hidden and input units to obtain optimal performance. The result of the analyses shows that there is effect on the network model when there is an increase in the number of hidden neurons, as well as the number of input units.
Regression and neural networks analysis in vesco-vaginal fistula causality: a comparative approach
(2012) James, T. O.; Udomboso, C. G.; Onwuka, G. I.
Vesico vaginal fistula (WF) is an abnormal opening of the vaginal wall to the bladder or rectum resulting in the leakage of urine. It is one of the worst morbidities associate with delivery and is a major public health problem on the rise with an estimated minimum of 150,000-200,000 patients in Nigeria. Neural network are able to solve the nonlinear regression problem. Very little research has been conducted to model the causes of WF using artificial neural networks. The data set obtained from the case records of women admitted with cases of Vesico-vaginal Fistula (WF) in Maryam Abacha Women and Children Hospital Sokoto, from January 2000 to December 2010 was used. We then compared the performance of Statistical neural networks and Regression model. In comparison to traditional methods, the value of Obstructed labour and misuse of instrument in ANN has higher R square (0.8 & 0.54) in which is a better result, lower MSE (2011 &4S79.6) which is also a better result. The p-value is only greater than 0.05 in obstructed labour. The results of the t and F statistics confirms the better performance, since any p-value lesser than 0.05 shows that that cause of WF cases is very significant. Therefore, we can accept the fact that MISUSE OF INSTRUMENT and YANKAN GISHIRI are both significant to cases of WF using ANN, while LR is not since the R squares are low. Statistical neural network model showed better predictions than various regression models for causes of WF. However, both methods can be used for the prediction of causes of WF.