Prediction of pulmonary tuberculosis treatment outcome in a sub-Saharan African context.

Failure to treat many pathogens is a concern. Identifying a priori, patients with potential failure treatment outcome of a disease could allow measures to reduce the failure rate. The objectives of this study were to use the Scoring method to identify factors associated with the tuberculosis unsuccessful treatment outcome and to predict the treatment outcome.

A fuzzy multifactor asset pricing model.

This paper introduces a new approach of multifactor asset pricing model estimation. This approach assumes that the monthly returns of financial assets are fuzzy random variables and estimates the multifactor asset pricing model as a fuzzy linear model. The fuzzy random representations allows us to incorporate bias on prices induced by the market microstructure noise and to reflect the intra-period activity in the analysis.

Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities.

Over the past few years, the application of deep learning models to finance has received much attention from investors and researchers. Our work continues this trend, presenting an application of a Deep learning model, long-term short-term memory (LSTM), for the forecasting of commodity prices. The obtained results predict with great accuracy the prices of commodities including crude oil price (98.