https://www.selleckchem.com/products/U0126.html
In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following (Step I) determining the optimal climate feature which can achieve good precision score ( 70%) when predicting the spatial classes distribution of the
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