Gross Enrolment Ratio Prediction using Artificial Neural Network

The present paper deals with Gross Enrolment Ratio (GER) prediction in higher education within the state of Mizoram, India. The data used in this study are obtained from the yearly report of All India Survey on Higher Education (AISHE), published by Ministry of Human Resource Development (MHRD), Govt. of India and Statistical Handbook of various years published by Dept. of Economics & Statistics, Govt. of Mizoram. In this study, a soft computing technique known as Artificial Neural Network (ANN) is implemented for prediction of GER in higher education. The data obtained are analyzed and categorized into two classes known as the input and target data. The input data represent the years from 1968 to 2017. The target data represents the enrolment details corresponding to the input year such as, male enrolment, female enrolment, eligible population and GER. After generating these input data and target data, an ANN is used for building a model. The model has been trained and tested using 530260 student enrolment data for the period of 50 years. In order to obtain an accurate GER prediction, the accuracy of four architectures of ANN known as Back Propagation (BP), Radial Basis (RB),