RESPOND OF SMART CITIES TO PANDEMIC COVID-19

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dc.contributor.authorBayat, Mohammad Nawid
dc.date.accessioned2023-05-25T10:23:57Z
dc.date.available2023-05-25T10:23:57Z
dc.date.issued2023
dc.description.abstractOver the past several years, smart cities have gained widespread recognition worldwide because they facilitate a better life for people and bring substantial changes in society; more specifically, smart cities impact the lives of citizens amid the pandemic COVID-19. The COVID-19 epidemic has affected many elements of life, including economics, education, healthcare, politics, and financial planning. However, case studies of 120 cities have been examined concerning their responses to pandemics, the pillars of smart cities, and the number of COVID cases. The study investigates how pandemic preparedness in smart cities could be improved by stopping the spread of the virus, finding treatment (vaccine), and remotely providing education and business continuity. Smart city solutions are desperately needed to deal with the multiple challenges of controlling the COVID-19 epidemic. One of the approaches is to forecast the spread of the virus and its effects on various areas of urban life using machine-learning models. We can test the effectiveness of different regression models in both scenarios using a dataset that contains relevant variables for the COVID-19 and smart city pillars. The prognostic accuracy of the Elastic Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and MLP Regression models for the prediction of COVID-19 infection rates based on smart city characteristics will be assessed in this way. The Random Forest Model outperformed Ridge Regression after adjusting model parameters and hidden layers (R-squared score 0.88 and 0.85, respectively). The MAE and MSE scores obtained using the Random Forest Regression technique are 0.009692 and 0.000261, respectively. On the other hand, the unsupervised ML Elbow technique separates the COVID data into five groups. The Elbow technique results in different records for each cluster, with the first cluster [0] including the most countries (171), the second and third clusters each comprising one country, the fourth cluster containing six countries, and the last cluster containing three countries. It explains why the majority of nations have comparable COVID-affected data and comparable pandemic prevention strategies and measures. The World Health Organization found that when cities and people had easier access to smart city features, the number of cases and deaths in each city reduced, while the rate of recovery and immunization climbed dramatically. Due to the limited data available, the acquired results do not confirm that each smart city responds to pandemics in the same way.en_US
dc.identifier.citationBayat, M. N. (2023). Respond of Smart Cities to pandemic COVID-19. School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7080
dc.language.isoenen_US
dc.publisherSchool of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Embargoen_US
dc.subjectIoTen_US
dc.subjectSmart Sustainable Citiesen_US
dc.subjectSmart Technologiesen_US
dc.subjectSocial Distancingen_US
dc.subjectFacial Masken_US
dc.subjectMachine Learningen_US
dc.subjectRemotely employment and learningen_US
dc.subjectCOVID-19en_US
dc.subjectBlockchainen_US
dc.titleRESPOND OF SMART CITIES TO PANDEMIC COVID-19en_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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