FACULTY OF SCIENCE

Permanent URI for this communityhttps://repository.ui.edu.ng/handle/123456789/266

Browse

Subject: search.filters.subject.Neural Network

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Performance evaluation of classification algorithms on academic performance of postgraduate students
    (2023-02) Okunlola, O. A.; Ojo, A. K.
    Educational data mining has contributed to enhancing student academic performance by way of enabling stakeholders in academic institutions to have a pre-knowledge of the risks and dangers ahead and how to mitigate them. Prediction algorithms perform differently on dataset, and so, the need to develop models using different prediction algorithms and evaluating the result of such predictions is very important in order to be sure the best algorithm for a particular dataset is used. This work employed four classifiers: K-Nearest-Neighbour, Neural Network, Naïve Bayes and Decision Tree to model and, evaluated their models to know the performance of each on the target dataset. Their results were evaluated based on the various performance metrics. The results showed that Decision Tree had the highest accuracy on the dataset with test accuracy of 48.25% and therefore is the most suitable out of the four classifiers for performing prediction modelling on the dataset. Naïve Bayes is the second-best prediction model that can be used for predicting academic performance with an accuracy of 36.25%., followed by Neural Network with accuracy of 32.5 % and then K-Nearest Neighbour with accuracy of 32.5% but with lower precision, recall and area under Receiver Operating Characteristic curve.
  • Thumbnail Image
    Item
    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.