Distributed Parallel Computing in Data Analysis of Osteoporosis.

This research aimed to compare the performance of two models of load balancing (Proportional and Autotuned algorithms) of the JPPF platform in the processing of data mining from a database with osteoporosis and osteopenia. When performing the analysis of execution times, it was observed that the Proportional algorithm performed better in all cases.

A Comparative Study of Bayes Net, Naive Bayes and Averaged One-Dependence Estimators for Osteoporosis Analysis.

This paper presents an evaluation of the accuracy of the Bayesian classifiers: Bayes Net, Naive Bayes and Averaged One-Dependence Estimator, to support diagnoses of osteopenia and osteoporosis. All classifiers showed good results, thus, given data, it is possible to produce a reasonably accurate estimate of the diagnosis.

Classification of images acquired with colposcopy using artificial neural networks.

Objective: To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy.

Purpose: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy.

Using a model of parallel distributed processing associated with data mining in the characterization of sexuality in a university population.

Using the framework for developing parallel applications Java Parallel Programming Framework were conducted performance analysis of an application for the clustering data by the method of fuzzy logic combined with Gustafson-Kessel algorithm. In addition to running in a distributed environment, for comparative purposes, were also conducted collections of processing time in environments with a single Personal Computer approach. With the results obtained by collecting time of application, there was a statistical analysis to validate the application and the algorithm as well as the use of computational clustering as a way to increase performance applications.