FORCAST THE STUDENT PERFORMANCE USING NEURO FUZZY SYSTEM
Author(s):
Dr.M. Balamurugan, R. Prasanka
Keywords:
Artificial Neural Network, Multilayer Perceptron, Back Propagation, Neuro Fuzzy system, Student Acquired Knowledge
Abstract
This work explores the prediction of students’ success in campus interview. It allows prediction of students acquired knowledge based on more or less of their qualitative observations of the writing test and viva model. Sorting out and predicting students acquired knowledge using arithmetic and statistical techniques may not necessarily provide the best means to assess the knowledge and skills. The neural networks, that have effective learning algorithms, had been accessible to endorse the development of tuning fuzzy systems. A Neuro fuzzy neural network model effectively handles reasoning with correct data, and enables representation of student acquired knowledge. The Artificial neural network (ANN) is a multilayer perceptron (MLP) with one input layer, two hidden layers and one output layer and it was trained using a version of the flexible back propagation algorithm. The input data are the student's writing test and viva question results at the time of enrolling at the campus interview. The ability to predict student's results is of great help for the college management in order to take early action to avoid what is the best interview test of the student's education.
Article Details
Unique Paper ID: 143263

Publication Volume & Issue: Volume 1, Issue 8

Page(s): 129 - 133
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