Browsing by Author "Honqwu, Q."

Now showing 1 - 3 of 3
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    COMPARING THE PERFORMANCE OF PREDICTIVE MODELS CONSTRUCTED USING THE TECHNIQUES OF FEED-FORWARD AND GENERALIZED REGRESSION NEURAL NETWORKS
    (Universiti Malaysia Pahang, 2016-02) Ajiboye, A.R.; Abdullah-Arshah, R.; Honqwu, Q.; Abdul-Hadi, J.
    Construction of predictive model is primarily aimed at using the known attributes to determine the present or the future unknown attributes for efficient planning and decision making. The accuracy of predictive model is therefore, paramount to achieving network outputs that are well correlated with the known or target output. In this paper, two predictive models are constructed using the techniques of feed-forward and generalized regression neural networks. Experiments are conducted with a Matlab software and the performance of the two models is evaluated for accuracy. Their simulated outputs are compared to determine their response to untrained data. Findings from this study show that, the generalized regression neural network consistently shows a more accurate result. The Mean Absolute Error computed for the two models also reveals that, feed-forward neural network records higher error value.
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    Risk Status Prediction and Modelling Of Students’ Academic Achievement - A Fuzzy Logic Approach
    (2013-11) Ajiboye, A.R.; Abdullah-Arshah, R.; Honqwu, Q.
    Several students usually fall victims of low grade point at the end of their first year in the institution of higher learning and some were even withdrawn due to their unacceptable grade point average (GPA); this could be prevented if necessary measures were taken at the appropriate time. In this paper, a model using fuzzy logic approach to predict the risk status of students based on some predictive factors is proposed. Some basic information that has some correlations with students’ academic achievement and other predictive variables were modelled, the simulated model shows some degree of risk associated with their past academic achievement. The result of this study would enable the teacher to pay more attention to student’s weaknesses and could also help school management in decision making, especially for the purpose of giving scholarship to talented students whose risk of failure was found to be very low; while students identified as having high risk of failure, could be counselled and motivated with a view to improving their learning ability.
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    Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
    (International Scientific Academy of Engineering & Technology., 2015-05) Ajiboye, A.R.; Abdullah-Arshah, R.; Honqwu, Q.
    The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experience some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of failure and low Grade Point Average (GPA). Computing the earlier academic achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-informed of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network for the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are created using the existing and enhanced feed-forward neural network techniques. The ability of these models to generalize is measured using simulation methods. The enhanced nefwork model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supported financially in the form of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies.