Education System re-engineering with AI (artificial intelligence) for Quality Im-provements with proposed model

Abstract

Re-engineering (RE) of existing educational institutions (EI) with adoption of latest technology trends (LTT) in form of artificial intelligence (AI) can be great effective in term of quality systems. Increase in student's strength in class and terrorist attacks on EI urged us to introduce such approach that can assure education quality. Class monitoring with heavy strength always remain major issue for teacher during lecture delivery. In this paper, we implemented reengineering using artificial intelligence based two theories of 1) Multi-face recognition (MFR) system 2) Facial expression recognition (FER) system. Both of these theories supported by intelligent techniques as principal component analysis (PCA), discrete wavelet transform (DWT) and k-nearest neighbor (KNN). After implementation of these intelligent techniques student's attentiveness will increase. Our developed system can detect expressions like happiness, repulsion, fear, anger, and confusion. Student's attentiveness score will be displayed on screen. Teacher can interpret on the basis of attentiveness %age. System decision making can be helpful for class continuity or short break. This system is also an application of an expert system (ES) and knowledge base system (KBS) for educational quality assurance. A similar monitoring system was imposed in china with Hikvision Digital Technology. Predations results proved monitoring can be best way for education quality.