Addressing the COVID-19 Crisis by Harnessing Internet of Things Sensors and Machine Learning Algorithms in Data-driven Smart Sustainable Cities

Q1 Arts and Humanities

Geopolitics, History, and International Relations Pub Date : 2020-01-01 DOI:10.22381/ghir12220209

Nancy Lyons, George Lăzăroiu

引用次数: 29

Abstract

This paper analyzes the outcomes of an exploratory review of the current research on data-driven smart sustainable cities The data used for this study was obtained and replicated from previous research conducted by Capgemini, ICMA, KPMG, UNESCAP, UNHSP, SCC, The University of Adelaide, and The World Bank We performed analyses and made estimates regarding Internet of Things sensors and machine learning algorithms Data collected from 5,200 respondents are tested against the research model by using structural equation modeling © 2020, Addleton Academic Publishers All rights reserved

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在数据驱动的可持续智慧城市中利用物联网传感器和机器学习算法应对COVID-19危机

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Geopolitics, History, and International Relations Arts and Humanities-History

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期刊介绍： Geopolitics, History, and International Relations publishes mainly original empirical research and review articles focusing on hot emerging topics, e.g. digital diplomacy, online political participation, data activism, fake social media news, algorithmic governance, computational politics, Internet terrorism, autonomous weapons systems, virtual history, innovative data-driven smart urban ecosystems, etc. This journal considers only manuscripts having a high integrative value in the current Scopus- and Web of Science-indexed literature.