A Proposed Artificial Intelligence Algorithm for Development of Higher Education

Abstract

Higher education has delved into a new stage of rapid development focusing on quality improvement, while encountering new challenges and obstacles. In this research work, an artificial intelligence algorithm for education improvement is proposed. Firstly, deep feature abstraction in temporal and special dimensions is performed using Long Short-Term Memory (LSTM) artificial neural network and convolutional networks. Consequently, multiscale attention fusion techniques are used to improve the articulateness of the characteristics and come up with better recommendations with the assistance of multilayer perceptron. Moreover, the proposed model helps in improving the cognitive capability of students and enhances their overall quality of perception. Moreover, it has been proven that the performance of the proposed model provides better recommendation outcomes and better robustness compared to existing models through conducting extensive experiments based on real data.