eprintid: 3716 rev_number: 12 eprint_status: archive userid: 2 dir: disk0/00/00/37/16 datestamp: 2022-09-29 02:58:42 lastmod: 2023-07-12 23:30:58 status_changed: 2022-09-29 02:58:42 type: article metadata_visibility: show creators_name: Verma, Anil creators_name: Anand, Divya creators_name: Singh, Aman creators_name: Vij, Rishika creators_name: Alharbi, Abdullah creators_name: Alshammari, Majid creators_name: Ortega-Mansilla, Arturo creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: creators_id: creators_id: creators_id: arturo.ortega@uneatlantico.es title: IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0 ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Education 4.0; Industry 4.0; IoT (Internet of Things); IoT; Fog; Cloud Computing; M-Bi-LSTM; assessment; irregularity detection abstract: 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 date: 2022-04 publication: Electronics volume: 11 number: 9 pagerange: 1436 id_number: doi:10.3390/electronics11091436 refereed: TRUE issn: 2079-9292 official_url: http://doi.org/10.3390/electronics11091436 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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 metadata Verma, Anil; Anand, Divya; Singh, Aman; Vij, Rishika; Alharbi, Abdullah; Alshammari, Majid y Ortega-Mansilla, Arturo mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es (2022) IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0. Electronics, 11 (9). p. 1436. ISSN 2079-9292 document_url: http://repositorio.uneatlantico.es/id/eprint/3716/1/electronics-11-01436-v3.pdf