Mestrado em Modelagem e Otimização - PPGMO

URI Permanente para esta coleção

https://repositorio.ufcat.edu.br/handle/tede/40
O Mestrado em Modelagem e Otimização tem um papel essencial no desenvolvimento de novas tecnologias. Atualmente, se faz muito intensamente o uso de modelos matemáticos, simulações avançadas e sofisticados desenvolvimentos computacionais na pesquisa científica em geral.

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Navegando Mestrado em Modelagem e Otimização - PPGMO por Autor "Batista, Marcos Aurélio"

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    Estudo e desenvolvimento de métodos para predição de doadores de sangue
    (Universidade Federal de Goiás, 2018-02-16) Silva, Fernando Henrique da; Silva, Sérgio Francisco da; http://lattes.cnpq.br/9061098995683609; Silva, Sérgio Francisco da; http://lattes.cnpq.br/9061098995683609; Santos Filho, Tércio Alberto dos; Batista, Marcos Aurélio
    Hemotherapy units has difficulties to optimize the search for blood donors in emergency situations, as well as to keep their blood stocks at adequate levels. On the other hand, the use of computational techniques for prediction has obtained promissing results in several areas of knowledge, and can be seen as a fundamental tool in obtaining blood donations, however, are little explored in this sector. Given this gap, this research aimed to analyze and develop prediction techniques to optimize the search for donors with higher conversion rate to the donation, focusing on data mining techniques. For this, we first analyzed the performance of traditional literature classifiers applied to a real database, which produced unsatisfactory prediction results. Seeking for higher quality results we propose a top-k recommendation approach of blood donors, which uses heuristics to estimate a confidence degree in donation. Computational experiments show that the top-k recommendation approach achieves good results for all three developed heuristics. The support vector-based heuristic achieving 94.09% of precision among the top-10 recommended, and 99.90% of precision for top-1, for the same data set that the classifiers were not successful. It is expected that the results of this research will contribute to the academic community due to the variety of classifiers analyzed and especially due to the proposed top-k recommendations approach. In the future, this approach can be better analyzed with other databases and even improved by the development of new heuristics. In addition, it is believed that the developed top-k approach can be used in health prediction systems, with a focus on predicting blood donors, especially in emergency situations.
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    Redes neurais artificiais aplicadas à segmentação de imagens
    (Universidade Federal de Goiás, 2017-02-15) Albanez, Daniela de Oliveira; Batista, Marcos Aurélio; http://lattes.cnpq.br/3637940122788341; Batista, Marcos Aurélio; Barcelos, Célia Aparecida Zorzo; Silva, Sérgio Francisco da; Rabelo, Marcos Napoleão; Silva, Núbia Rosa da
    Image segmentation is one of image processing that problems that deserve special interest of the scientific community, given its real utility and application in various areas as medicine, geography, engineering, mathematics and computing, just to name a few. Much of the recent interest in segmentation has been motivated by the availability of satellite images on the Earth’s surface, which can be transformed into concrete knowledge, aiming at land use monitoring and data mining. This work proposes a new segmentation method, using Arficial Intelligence techniques, more specifically Artificial Neural Networks (ANNs), and compare its results of segmentation of satellite images with the original method. The binarized results are compared with the ground truth for the validation of the proposed segmentation method. Experimental results, quantative analysis of segmentation results, indicating that the proposed segmentation method generates better results and a decaying of 36.60% in the total average computacional time when compared with the original method.