What is it about?
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, multiplelayers, 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 layer
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Why is it important?
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, multiplelayers, 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 layer
Perspectives
A neural network model can be effectively employed to predict students’ academic performance using machine learning techniques. This study focuses on comparing forward and backward neural network architectures and developing models capable of communicating their outputs in the required format. Using background information from 580 engineering students, the study aims to predict Grade Point Average (GPA) based on various parameters, including mental health-related factors. For data preparation and feature selection, the study employs the Boruta algorithm and Random Forest methods. A total of 24 neural network configurations (12 × 2) were developed using single-layer and multilayer architectures, incorporating both forward and backward algorithms to evaluate GPA prediction performance and accuracy. The models were assessed using histograms, confusion matrices, and regression analysis.
Dr. YAGYANATH RIMAL
Pokhara University
Read the Original
This page is a summary of: The Comparison of Forward and Backward Neural Network Model – A Study on the Prediction of Student Grade, WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL, July 2021, World Scientific and Engineering Academy and Society (WSEAS),
DOI: 10.37394/23203.2021.16.37.
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