Open AccessPrediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System
Author(s) -
Imo Eyoh,
Edward N. Udo,
Ini Umoeka
Publication year - 2021
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2021/241022021
Subject(s) - artificial neural network , fuzzy logic , mean squared error , adaptive neuro fuzzy inference system , backpropagation , interval (graph theory) , time series , prediction interval , statistics , mean absolute error , artificial intelligence , neuro fuzzy , mathematics , computer science , fuzzy control system , combinatorics
COVID-19 is a virus known to emanate from Wuhan, China in December 2019. COVID-19 spread widely to nearby countries like Japan and Korea, followed by Europe and America and later to Africa. Particularly, South Africa and Egypt have been worst hit by the virus. Generally, the COVID-19 data is highly uncertain and requires fuzzy logic approaches for the effective handling of these uncertainties. This study therefore presents the prediction of COVID-19 cases in South Africa and Egypt using interval type-2 fuzzy logic system with Takagi-Sugeno-Kang fuzzy inference and neural network learning. The parameters of the model are adapted using gradient descent backpropagation approach. The proposed model is found to outperform type-1 fuzzy logic system and artificial neural network in terms of the root mean squared error, mean absolute percentage error and mean absolute error