PROGRAMA DE PÓS GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE - PPGEE

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Navegando PROGRAMA DE PÓS GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE - PPGEE por Assunto "Análise de Algoritmos e Complexidade de Computação"

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    Desenvolvimento de método de inteligência artificial baseado no comportamento de enxames do gafanhoto-do-deserto
    (Universidade Federal do Maranhão, 2017-02-20) RIBEIRO, Tiago Martins; PAUCAR, Vicente Leonardo; 213445538-18; http://lattes.cnpq.br/1155686983267102
    Complex optimization problems have been studied over the years by researchers seeking better solutions, these studies have encouraged the development of several algorithms of artificial intelligence, and a part of them are bio-inspired methods, based on the behavior of populations. These algorithms target to develop techniques based on nature in search of solutions to these problems. In this work, was introduced as a purpose, an algorithm based on the behavior of locust swarms, the Locust Swarm Optimizer (LSO). The behavior of the desert locust is introduced highlighting the formation of clouds of attacks caused by a synthesized neurotransmitter monoamine, present on the insect, known as serotonin. Observing this behavior, the LSO was developed. It was compared to other known artificial intelligence techniques through 23 benchmark functions and also tested on an power system economical dispatch problem. From the point of view of the results and the ease of implementation, it can be concluded that the LSO algorithm is very competitive as compared to existing methods
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    Metodologia computacional para a segmentação da próstata e classificação de lesões em imagens de ressonância magnética utilizando o modelo de Ising
    (Universidade Federal do Maranhão, 2019-03-11) REIS, Artur Bernardo Silva; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; CONCI, Aura; http://lattes.cnpq.br/5601388085745497; PACIORNIK, Sidnei; http://lattes.cnpq.br/4692086634018379; CARVALHO FILHO, Antonio Oseas de; http://lattes.cnpq.br/7913655222849728
    Prostate cancer is the second most prevalent type of cancer in the male population worldwide. The adoption of prostate imaging tests for the prevention, diagnosis, and treatment has grown. It is known that early detection increases the chances of an effective treatment, improving the prognosis of the disease. With this aim, computational tools have been proposed with the purpose of assisting the specialist in the interpretation of imaging tests, especially magnetic resonance imaging (MRI), providing the detection of lesions. The research of this doctoral work has as primary objective the proposition of an automatic methodology for the detection of lesions in the prostate. We divide the proposed methodology into two stages. In the first stage prostate segmentation is performed, for this purpose, the Ising model is used, models of probability, quality threshold and fusion of atlas labels. The second stage consists of the classification of abnormal tissues in the prostate. To this end, we extract lesion candidates through the Wolff algorithm, then texture characteristics are extracted using the Ising model, and finally, the vector machine is used to classify lesion or healthy tissue. The methodology was validated using three bases of T2-weighted MRI images. We used three bases for prostate segmentation. However, we used only one in prostate segmentation and lesion detection. The total number of images used in the validation of prostate segmentation was 108. The experimental results obtained here indicate an excellent perspective, considering that we obtained a mean Dice similarity coefficient (DSC) of 94.03 % in the step of. We validated The lesion detection stage on a set of 28 images with lesion markers. The methodology obtained a sensitivity of 95:92%, specificity of 93:89% and accuracy of 94:16%. These are promising since they were more significant than other methods compared.