This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-19 forecast developed by our research team during the first French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts of this disease, we conceived models for multiple indicators: daily or cumulative confirmed cases, hospitalizations, hospitalizations with artificial ventilation, recoveries, and deaths. In spite of the limited data available when the lockdown was declared, we achieved good short-term performances at the national level with a classical CNN for hospitalizations, leading to its integration into a hospitalizations surveillance tool after the lockdown ended. Also, A Temporal Convolutional Network with quantile regression successfully predicted multiple COVID-19 indicators at the national level by using data available at different scales (worldwide, national, regional). The accuracy of the regional predictions was improved by using a hierarchical pre-training scheme, and an efficient parallel implementation allows for quick training of multiple regional models. The resulting set of models represent a powerful tool for short-term COVID-19 forecasting at different geographical scales, complementing the toolboxes used by health organizations in France.
This paper describes a method of predicting wheat yields based on machine learning, which accurately determines the value of wheat yield losses in France. Obtaining reliable value from yield losses is difficult because we are tackling a highly unbalanced classification problem. As part of this study, we propose applying the Synthetic Minor Oversampling technique (SMOTE) as a pretreatment step before applying machine learning methods. The approach proposed here improves the accuracy of learning and allows better results on the set of tests by measuring the operating characteristic of the ROC receiver. The comparative study shows that the best result obtained is 90.07% on the set of tests, obtained by hybridizing the SMOTE algorithm with the Random Forest algorithm. The results obtained in this study for wheat yield can be extended to many other crops such as maize, barley, ...
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