4.6 Article

IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0

期刊

ELECTRONICS
卷 11, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/electronics11091436

关键词

Education 4.0; Industry 4.0; IoT (Internet of Things); IoT; Fog; Cloud Computing; M-Bi-LSTM; assessment; irregularity detection

资金

  1. Taif University Researchers Supporting Project [TURSP-2020/231]
  2. Taif University, Taif, Saudi Arabia

向作者/读者索取更多资源

Education 4.0 imitates Industry 4.0 by adopting advanced technologies such as IoT, Fog Computing, Cloud Computing, and AR/VR. This study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0, which addresses similar issues faced in Industry 4.0. Experimental simulations validate the superior performance of the proposed framework compared to other contemporary technologies used in Education 4.0.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据