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Can Different Impacts of Covid-19 on Different Countries Be Explained? a Preliminary Machine Learning Experiment

Capitolo di libro
Data di Pubblicazione:
2022
Abstract:
The hypothesis that we intend to investigate here is that the extent of the impact of Covid-19 on a given country can be explained starting from a set of indicators and by using machine learning methodologies. The purpose of this chapter is not to find a way to solve the problem in an optimal way. Rather, we aim at performing a preliminary study to verify whether the aforementioned hypothesis is viable. Should it turn out so, we wish to get awareness both of which are the problems that must be solved in order to arrive at a (sub-)optimal solution, and of what are the possible limitations of the method. We firstly create a suitable data set of indicators starting from different sources available on the internet. Then, we apply onto it an evolutionary algorithm that is able to extract a set of IF-THEN decision rules allowing us to relate the values of the parameters for the different countries to the different levels of impact of Covid-19 on them.
Tipologia CRIS:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
Different Impacts; Covid-19; Different Countries; Machine Learning
Elenco autori:
DE FALCO, Ivanoe; Tarantino, Ernesto; Scafuri, Umberto
Autori di Ateneo:
DE FALCO IVANOE
SCAFURI UMBERTO
TARANTINO ERNESTO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/439504
Titolo del libro:
Computational Intelligence for COVID-19 and Future Pandemics - Emerging Applications and Strategies
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