The Comparison of Forward and Backward Neural Network Model – A Study on the Prediction of Student Grade

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Abstract: A neural network model can be used effectively in predicting training accuracy using machine learning. Based on the comparison of forward and backward neural networks, coded to communicate their output in the requisite manner using machine language is the basis of the present study. With the help of students' background information, to predict the Grade Point Average (GPA) of 580 engineering students based on various parameters, including mental health. The study is based on the Boruta algorithm and the random forest methods for data preparation in the matrices (12 * 2 = 24) of single-layered, multiple-layers, and forward and reverse algorithms adopted to test the prediction and accuracy of the grade point average by analyzing histograms, confusion matrices, and regression analysis. This study suggests the best model for predictions with the help of artificial neuron network that has roughly half the number of single layers and with three hidden layers.

WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856 , Volume 16, 2021, Art. #37

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Saikat Gochhait, Yagyanath Rimal, Sakuntala Pageni, "The Comparison of Forward and Backward Neural Network Model – A Study on the Prediction of Student Grade," WSEAS Transactions on Systems and Control, vol. 16, pp. 422-429, 2021, DOI:10.37394/23203.2021.16.37
Saikat Gochhait, Yagyanath Rimal, Sakuntala Pageni. The Comparison of Forward and Backward Neural Network Model – A Study on the Prediction of Student Grade. WSEAS Transactions on Systems and Control. 2021;16:422-429. 10.37394/23203.2021.16.37
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