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Machine Learning Model For Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
2021
IEEE journal of biomedical and health informatics
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 associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time
doi:10.1109/jbhi.2021.3052134
pmid:33449891
pmcid:PMC8545165
fatcat:prb22kycmfgdhkt6477ylujf6e