%N 9
%A Anil Verma
%A Divya Anand
%A Aman Singh
%A Rishika Vij
%A Abdullah Alharbi
%A Majid Alshammari
%A Arturo Ortega-Mansilla
%K Education 4.0; Industry 4.0; IoT (Internet of Things); IoT; Fog; Cloud Computing; M-Bi-LSTM; assessment; irregularity detection
%J Electronics
%V 11
%D 2022
%T IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0
%L uneatlantico3716
%P 1436
%X Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0
%R doi:10.3390/electronics11091436