Open AccessMachine Learning for Covid19 Spreaders Identification
Author(s) -
. Nagaveni,
. Amaresh
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41865
Subject(s) - prosperity , multiplex , computer science , covid-19 , identification (biology) , artificial intelligence , operations research , econometrics , economics , engineering , economic growth , medicine , biology , bioinformatics , botany , disease , pathology , infectious disease (medical specialty)
In this paper, we present a way to deal with recognize COVID-19 spreaders using the assessment of the association between socio-social and money related characteristics with the number of sicknesses and passings achieved by the COVID-19 contamination in different countries. For this, we inspect the information of each country using the flighty associations approach, unequivocally by separating the spreaders countries subject to the separator set in 5-layer multiplex associations. That's what the results show, we get a request for the countries subject to their numerical characteristics in financial aspects, people, Gross Domestic Product (GDP), prosperity and air affiliations; where, in the spreader set there are those countries that have high, medium or bad qualities in the different traits; in any case, the point that all of the countries having a spot with the separator set offer is a high worth in air affiliations. Keywords: Complex networks, complex systems, COVID-19, multiplex networks, optimization, social networks