TCC - Bacharelado em Ciência da Computação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/415
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Resultados da Pesquisa
Item Classificação de imagens de textura geradas por gráficos de recorrências no problema de pessoas sofrendo ataques epiléticos(2019) Queiroz, Danielly de Moura Borba; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/7461629772562910Epilepsy is a neurological condition characterized by the occurrence of epileptic seizuresthat recur in variations. These seizures are clinical manifestations of an abnormal dis-charge of neurons, which are cells that make up the brain. Some features make earlydiagnosis of epilepsy a major challenge, even for the most experienced clinicians. Asmedical aid, there are tests such as electroencephalogram (EEG) represented by timeseries widely used in the diagnosis of epilepsy. Time series are present in various areasof study, such as medicine, biology, economics, among others. Your graphics exposehidden patterns and alter data such as texture patterns as well as those that can beused by texture extraction methods. In addition, there are several tools for extractingtime series information, one of which is the hit image, which is currently used to verifythe change of an unsigned pattern. This paper presents a study of texture descriptorsand classifiers in images of healthy and epileptic people generated by recurrence im-ages. The texture descriptors using this study were: Local Binary Models (LBP), LocalPhase Quantification (LPQ) and Gabor Filter Bank. To the best of our knowledge, nostudy has yet been performed, applying these descriptors to base recurrence imagesused in this work. The evaluation is performed through the average hit, precision, recalland f-measure rate resulting from the following classifiers: textit Random Forest, andtextit Support Vector Machine (SVM). The experiments showed that the SVM classi-fier using the LPQ descriptor showed promising results, obtaining 92.1% hit, recall andf-measure mean and for accuracy obtained 92.26%.Item Uma proposta de métricas para avaliar a efetividade da execução de testes de software(2018) Barreto, Pedro Pires; Furtado, Ana Paula Carvalho Cavalcanti; http://lattes.cnpq.br/5862330768739698; http://lattes.cnpq.br/3199247203599540The market demands high-quality software, which is delivered on time at the agreed cost. One of the main software industries’ worries is the assurance of the created product quality that has generated the establishment of the software development associated with the main concepts of software quality. Many companies have been investing in the testing process to prevent and identify defects. Software testing is an important ally in quality assurance, which justifies a portion of the development costs being related to testing activities. During the development lifecycle and the software maintenance, tests are executed with the purpose of ensuring that the number of defects is minimized before the final product delivers to the client. The purpose of this research is to develop an approach to evaluate the execution of software test. To achieve this goal, it was used the Goal-Question-Metric approach, which seeks to generate a set of metrics according to the objectives defined by the need of the situation. After the definition of the objectives to evaluate a software test execution, it was proposed the adoption of a set of metrics to facilitate the monitoring and improvement of the software tests execution. To validate the set of proposed metrics, a focus group was conducted with specialists in the area of software testing. Thus, this research offers contributions to the metrics used to evaluate the execution of software tests that currently means a piece with a high cost for software companies.Item Um estudo comparativo de técnicas para a classificação contextual de companhia para sistemas de recomendação sensíveis a contexto(2019-01-22) Silva, Douglas Henrique Santana da; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/6428879549861854Nowadays, the vast amount of information has harmed users during decision making. In face of this problem, recommendation systems have been proposed in order to offer suggestions that help users to overcome such problem. These suggestions are even more valuable when these systems begin to suggest items based on the user contexts. Among these contexts, the companion context can be highlighted. Through the inference of the companion context the system may suggest different items if the user is accompanied or not. An example of a system that has such features is the CD-CARS. However, the unsupervised learning method for companion inference on CD-CARS has some limitations. In this way, the present research analyzed and highlighted a supervised learning method that can replace the current company contextual classification approach executed in the CD-CARS.