An Effective Utilization of Optimal Deep Learning Model Based Student Performance Prediction

Predicting student’s performance is exceptionally difficult because of the enormous measure of information accessible in scholarly data sets. The main objective of this paper is to predict the student performance with high prediction accuracy. For that an effective utilization of optimal deep learning model based on student performance prediction is proposed in this paper. At first, the input data is pre-processed. Then the pre-processed dataset is grouped by means of mini batch k means clustering algorithm. Finally, using Optimal Deep Neural Network (ODNN) the prediction is done. An Adaptive Artificial Flora optimization Algorithm (AAFO) is used to adapt the traditional deep neural network. The proposed model attains the maximum prediction accuracy of 97.86%. Using MATLAB platform the proposed model is implemented.