Exploiting the Core Academic Performance Prediction Parameters Using a Neuro-Fuzzy Model
Journal Title: American journal of Engineering Research - Year 2017, Vol 6, Issue 6
Abstract
This work developed a Neuro-Fuzzy model as an intelligent computational framework to provide decision support for the admission of candidates into Higher Institutions. The traditional admission process is based on Unified Tertiary Matriculation Examination (UTME) and Post- Unified Tertiary Matriculation Examination (PostUTME) scores, so long as the O-Level requirements are satisfied. This method has proven to have limitations. In this work, a five layer Adaptive Neuro-fuzzy Inference System (ANFIS) is modeled using a fuzzy logic input decision variables, taking into consideration the aggregate previous academic performances of the candidate and other related parameters that can influence academic performance. Historical records of students’ academic performances are used to build the fuzzy logic decision tree from which the initial fuzzy logic rules are extracted. The MATLAB NeuroFuzzy Designer is used for the modeling and training of the ANFIS model. Validation of the accuracy of the prediction using the affected student’s year 1, 2 , 3, 4 and 5 CGPAs, give the coefficients of multi determination to be approximately 0.96, 0.97 , 0.99, 0.98 and 0.96 respectively. These results show very high degree (97.2%) of accuracy, between the predicted and actual performance. These findings show that the developed model can be relied upon to make decisions on the admission of candidates into HAILs in any country of the world.
Authors and Affiliations
Okereke Eze Aru,, Nkwachukwu Chukwuchekwa,, F. K. Opara
Keywords
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