chunk1

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.

Keywords: COVID-19; Internet of Things; big data; smart sustainable city; algorithm

How to cite: Lyons, N., and Lăzăroiu, G. (2020). “Addressing the COVID-19 Crisis by Harnessing Internet of Things Sensors and Machine Learning Algorithms in Data-driven Smart Sustainable Cities,” Geopolitics, History, and International Relations 12(2): 65–71. doi:10.22381/GHIR12220209

Received 12 July 2020 • Received in revised form 18 October 2020
Accepted 23 October 2020 • Available online 27 October 2020

Nancy Lyons
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Industrial Big Data Analytics
at AAER, Edinburgh, Scotland
(corresponding author)
George Lăzăroiu
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Institute of Smart Big Data Analytics,
New York City, NY, USA;
Spiru Haret University, Bucharest, Romania

Home | About Us | Sales | Author's Page | Journals | Abstracting & Indexing | Contributors | Books | Contact | Online Access

© 2009 Addleton Academic Publishers. All Rights Reserved.

 
Joomla templates by Joomlashine