Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associate...

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Bibliographic Details
Main Authors: De Oliveira Serra, G.L (Author), Dos Santos Gomes, D.C (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Adaptive similarities
algorithm
Algorithms
Article
Clustering algorithms
Comparative analysis
Computational model
computer model
computer simulation
Computer Simulation
coronavirus disease 2019
covid-19
COVID-19
Crack propagation
epidemic
epidemiological data
epidemiology
forecasting
Forecasting
Fuzzy clustering
Fuzzy filters
fuzzy logic
Fuzzy Logic
fuzzy system
genetic algorithm
human
Humans
Intelligent machine
interval type 2 fuzzy kalman filter
Interval type-2 fuzzy
interval type-2 fuzzy systems
isolation and purification
kalman filtering
machine learning
Machine learning
Machine Learning
Machine learning models
mathematical model
measurement accuracy
orthogonal rotation
principal component analysis
priority journal
random forest
Real-time forecasting
SARS-CoV-2
Spectral decomposition
spectroscopy
time series analysis
Tracking and forecasting
training
virology
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