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      An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

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          Abstract

          The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

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          Intuitionistic fuzzy sets

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            Is Open Access

            Pandemics, tourism and global change: a rapid assessment of COVID-19

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              The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China

              Abstract Background With its epicenter in Wuhan, China, the COVID-19 outbreak was declared a public health emergency of international concern (PHEIC) by the World Health Organization (WHO). Consequently, many countries have implemented flight restrictions to China. China itself has imposed a lockdown of the population of Wuhan as well as the entire Hubei province. However, whether these two enormous measures have led to significant changes in the spread of COVID-19 cases remains unclear. Methods We analyzed available data on the development of confirmed domestic and international COVID-19 cases before and after lockdown measures. We evaluated the correlation of domestic air traffic to the number of confirmed COVID-19 cases and determined the growth curves of COVID-19 cases within China before and after lockdown as well as after changes in COVID-19 diagnostic criteria. Results Our findings indicate a significant increase in doubling time from 2 days (95% Confidence Interval, CI): 1.9–2.6), to 4 days (95% CI: 3.5–4.3), after imposing lockdown. A further increase is detected after changing diagnostic and testing methodology to 19.3 (95% CI: 15.1–26.3), respectively. Moreover, the correlation between domestic air traffic and COVID-19 spread became weaker following lockdown (before lockdown: r = 0.98, p < 0.05 vs. after lockdown: r = 0.91, p = NS). Conclusions A significantly decreased growth rate and increased doubling time of cases was observed, which is most likely due to Chinese lockdown measures. A more stringent confinement of people in high risk areas seem to have a potential to slow down the spread of COVID-19.
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                Author and article information

                Contributors
                abbas.mardani.edu@gmail.com
                Journal
                Artif Intell Rev
                Artif Intell Rev
                Artificial Intelligence Review
                Springer Netherlands (Dordrecht )
                0269-2821
                1573-7462
                3 June 2021
                : 1-26
                Affiliations
                [1 ]GRID grid.413021.5, ISNI 0000 0004 0612 8240, Faculty of Economics, Management, and Accounting, , Yazd University, ; Yazd, Iran
                [2 ]GRID grid.170693.a, ISNI 0000 0001 2353 285X, University of South Florida, ; Tampa, FL United States
                [3 ]GRID grid.258806.1, ISNI 0000 0001 2110 1386, Kyushu Institute of Technology, Department of Artificial Intelligence, ; 680-4 Kawazu, Lizuka, Fukuoka, 820-8502 Japan
                [4 ]Department of Mathematics, Govt. College Jaitwara, Satna, MP India
                [5 ]GRID grid.419655.a, ISNI 0000 0001 0008 3668, Department of Mathematics, , National Institute of Technology, ; Warangal, 506004 India
                Article
                10029
                10.1007/s10462-021-10029-9
                8173517
                34103781
                fe85eb80-3422-4cd2-82e2-cf3537999e1f
                © The Author(s), under exclusive licence to Springer Nature B.V. 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 28 May 2021
                Categories
                Article

                adapted online education,higher education institutions,hesitant fuzzy sets,multi-criteria decision making (mcdm),fuzzy sets

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