ANFIS-inspired smart framework for education quality assessment

Abstract

In the education sector, the Internet of Things (IoT) technology, integrated with fog-cloud computing, has offered productive services. Motivated by this, the smart recommender system offers the facility to the students to opt for the course and college based on the education quality. This research provides an IoT-fog-cloud paradigm for evaluating the academic environment with a perspective to enhance quality education. Specifically, IoT technology is incorporated to gather data about the academic environment that directly and indirectly influence the quality of education. Using the Bayesian Modeling Technique, the data collected is analyzed utilizing a fog-cloud computing framework to quantify the measure of the probability of education quality (PoEQ). Moreover, the Education Quality Assurance Index (EQAI) is calculated to analyze the quality assessment over a temporal scale. Furthermore, predictive decision-making is performed for quality estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The experimental simulation on 4 challenging datasets namely C1 (2124 instances), C2 (2112), C3 (2139), and C4 (2109) shows the effectiveness of the proposed framework. Simulation findings are compared with state-of-the-art techniques to measure the overall performance enhancement of the proposed system. Also, the mathematical analysis was carried out to assess the analytical performance of the proposed framework.