A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing
© 2021 The Authors. Expert Systems published by John Wiley & Sons Ltd.
| Veröffentlicht in: | Expert systems. - 1998. - 39(2022), 3 vom: 27. März, Seite e12704 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Expert systems |
| Schlagworte: | Journal Article COVID cloud computing corona deep neural networks fog computing pandemic |
| Zusammenfassung: | © 2021 The Authors. Expert Systems published by John Wiley & Sons Ltd. A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19 |
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| Beschreibung: | Date Revised 04.10.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1468-0394 |
| DOI: | 10.1111/exsy.12704 |