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BOSS: A new QoS aware blockchain assisted framework for secure and smart healthcare as a service.
- Source :
- Expert Systems; May2023, Vol. 40 Issue 4, p1-19, 19p
- Publication Year :
- 2023
-
Abstract
- The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664720
- Volume :
- 40
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Expert Systems
- Publication Type :
- Academic Journal
- Accession number :
- 163094789
- Full Text :
- https://doi.org/10.1111/exsy.12838