This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used to assess the disease diagnosis. Shapley additive explanations were used to explain predictions for the black-box models, while glass-box models feature importance brought insights into the most relevant features. All global explanations show the eosinophils and leukocytes, white blood cells are among the essential features to help diagnose the COVID-19. Moreover, the best model obtained an AUC of 0.87.

This paper proposes the use of artificial neural networks to predict the flexural resistance and initial stiffness of beam-to-column steel joints using the back propagation supervised learning algorithm. Three types of steel beam-to-column joints were investigated: welded, endplate and bolted with top, seat and double web angles, respectively. The neural networks results proved to be consistent with experimental and design code reference values

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