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Acute Respiratory Tract Infections are among the leading causes of child mortality worldwide. Specifically, community-acquired pneumonia has different causes, such as: passive smoking, air pollution, poor hygiene, cardiac insufficiency, oropharyngeal colonization, nutritional deficiency, immunosuppression, and environmental, economic and social factors. Due to the variation of these causes, knowledge discovery in this area of health has been a great challenge for researchers. Thus, this paper presents the steps for the construction of a database and evaluation results applied to the analysis and prediction of potential deaths caused by childhood pneumonia using the Pictorea method. For this, the Random Forest and Artificial Neural Network algorithms were used, and after comparison, the Neural Network algorithm showed higher accuracy by up to 87.57%. This algorithm was used to analyze and predict the number of deaths from pneumonia in children up to 5 years old, and the results were presented using Root Mean Square Error and scatter plots. A domain specialist validated the results and defined that the pattern found is relevant for future studies in the medical field, helping to analyze the behavior of countries and predict future scenarios.

O uso de programação paralela torna-se cada vez mais essencial, bem como o uso de ferramentas que auxiliam o programador nas tarefas de paralelismo e obtenção do melhor desempenho da arquitetura utilizada. Assim, o objetivo deste trabalho é a criação de uma otimização de paralelização de código em C/C++ que utiliza Aprendizagem por Reforço para realizar a paralelização automática de código em GPU utilizando a linguagem CUDA. Os experimentos realizados mostraram que a otimização criada é capaz de realizar a paralelização automática em CUDA, conseguindo altos speedups.

The amount of data generated on the Web has increased dramatically, as well as the need for computational power to prepare this information. In particular, indexers process these data to extract terms and their occurrences, storing them in an inverted file, a compact data structure that provides quick search. However, this task involves processing of a large amount of data, requiring high computational power. In this article, we present a heterogeneous parallel architecture that uses CPU and GPU in a cluster to accelerate inverted index generation. Experimental results show that the proposed architecture provides faster execution times, up to 60 times in classification and 23 times in the compression of 1 million elements.