Keyword search (3,448 papers available)


Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.

Author(s): Khalilpourazari S, Hashemi Doulabi H

World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable pol...

Article GUID: 33424076
Investigation into the oil removal from sand using a surface washing agent under different environmental conditions.

Author(s): Bi H, An C, Chen X, Owens E, Lee K

Spilled oil frequently reaches the shorelines and affects coastal biota and communities. The application of surface washing agents is an important shoreline cleanup technique that can help remove stranded oil from substrate surfaces with the advantages of h...

Article GUID: 32829266
Title:Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.
Authors:Khalilpourazari S,  Hashemi Doulabi H
Link:https://www.ncbi.nlm.nih.gov/pubmed/33424076
DOI:10.1007/s10479-020-03871-7
Category:Ann Oper Res
PMID:33424076
Dept Affiliation: ENCS
1 Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Canada.
2 Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada.

Description:

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.

Ann Oper Res. 2021 Jan 03; :1-45

Authors: Khalilpourazari S, Hashemi Doulabi H

Abstract

World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

PMID: 33424076 [PubMed - as supplied by publisher]