RETRACTED: Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage
ISSN: 1708-5284
Article publication date: 7 June 2021
Issue publication date: 22 February 2022
Retraction statement
The publishers of World Journal of Engineering wish to retract the article Singh, P. and Kaur, R. (2022), “Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage”, World Journal of Engineering, Vol. 19 No. 1, pp. 80-89. https://doi.org/10.1108/WJE-12-2020-0636
An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions.
The publishers of the journal sincerely apologize to the readers.
Abstract
Purpose
The purpose of this paper is to provide more accurate structure that allows the estimation of coronavirus (COVID-19) at a very early stage with ultra-low latency. The machine learning algorithms are used to evaluate the past medical details of the patients and forecast COVID-19 positive cases, which can aid in lowering costs and distinctively enhance the standard of treatment at hospitals.
Design/methodology/approach
In this paper, artificial intelligence (AI) and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A delay-sensitive efficient framework for the prediction of COVID-19 at an early stage is proposed. A novel similarity measure-based random forest classifier is proposed to increase the efficiency of the framework.
Findings
The performance of the framework is checked with various quality of service parameters such as delay, network usage, RAM usages and energy consumption, whereas classification accuracy, recall, precision, kappa static and root mean square error is used for the proposed classifier. Results show the effectiveness of the proposed framework.
Originality/value
AI and cloud/fog computing are integrated to strengthen COVID-19 patient prediction. A novel similarity measure-based random forest classifier with more than 80% accuracy is proposed to increase the efficiency of the framework.
Keywords
Citation
Singh, P. and Kaur, R. (2022), "RETRACTED: Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage", World Journal of Engineering, Vol. 19 No. 1, pp. 80-89. https://doi.org/10.1108/WJE-12-2020-0636
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited