Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset

Abstract

Designing an optimal neural network architecture plays an important role in the performance of a neural network model. In the past few years, various bio-inspired optimization techniques have been applied to find the optimal architecture of a neural network model. In this paper particle swarm optimization (PSO) technique has been applied with recurrent neural network long short-term memory (LSTM) algorithm to find an optimal architecture for feed forward neural network. To optimize the architecture of neural network model Parameters considered are hidden neurons, learning rate and activation function. Fitness function applied for the selection of the optimal combination of the parameters is root mean square error (RMSE). Due to privatization of education number of private institutes and universities are increasing rapidly every year. This increase has resulted in huge number of data (NAAC reports) regarding the assessment and accreditation of higher education institutions. Dataset of 500 educational institutes has been collected from the official site of National Assessment and Accreditation Council (NAAC). Hybrid model of PSO with LSTM algorithm has been applied to the educational dataset. Selection of the optimal architecture is done on the basis of RMSE, Accuracy and other performance parameters.