TY - JOUR SN - 2079-9292 UR - http://doi.org/10.3390/electronics11091436 VL - 11 IS - 9 Y1 - 2022/04// N2 - 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 JF - Electronics KW - Education 4.0; Industry 4.0; IoT (Internet of Things); IoT; Fog; Cloud Computing; M-Bi-LSTM; assessment; irregularity detection A1 - Verma, Anil A1 - Anand, Divya A1 - Singh, Aman A1 - Vij, Rishika A1 - Alharbi, Abdullah A1 - Alshammari, Majid A1 - Ortega-Mansilla, Arturo AV - public TI - IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0 ID - uneatlantico3716 ER -