TCC - Bacharelado em Ciência da Computação (UAG)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2952
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Item Análise dos componentes principais supervisionada: uma abordagem não-paramétrica(2019-02-04) Sousa, Raul Pedro de Vasconcelos; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500; http://lattes.cnpq.br/3362844917050042Problems of classification of data become more commonly used. Classification task has a broader range of applications, ranging from detection of spam emails to classification of malignant and benign tumors. In these problems, the quantity of characteristics plays a fundamental role both in the quality and performance of the classifiers. Data having a high dimensionality tends to have lower accuracy and longer processing time. Feature extraction techniques are excellent solutions to this situation, generating a new set of features and selecting the best ones for classification. Principal Component Analysis (PCA) is one of the most common feature extraction techniques. In general, PCA presents excellent results, but because it is an unsupervised technique there are situations where the method can not extract discriminant features. We developed a supervised version of the PCA using Bayesian classification with the kernel density estimation (KDE) to select features. This method has emerged as an extension of the Minimum Classification Error PCA (MCPCA). MCPCA also uses the Bayesian error as a metric however it presents a series of constraints. Comparing the exposed method with PCA, MCPCA and Supervised PCA (SPCA), another supervised approach to PCA, comparing the accuracy by characteristics in four classifiers to sixteen databases. The proposed method presented the greater accuracy in 72% of the cases. For PCA, MCPCA, and SPCA this number is 31%, 36%, 12%, respectively. When using a single extracted feature, the maximum accuracy if achieved is 89%, 14%, 37%, and 25% of the cases for proposed method, PCA, MCPCA, and SPCA, respectively.Item Desenvolvimento de sistema web mobile para home care: beira leito(2018-08-23) Sousa, Raul Pedro de Vasconcelos; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500; http://lattes.cnpq.br/3362844917050042This report presents "Beira Leito", a web-mobile system for home care, which assists in the control of the day and stock for the company "Homecare Garanhuns". Home care is a form of medical care where the patient is hospitalized, receiving periodic visits from the health professionals who accompany him, such as: doctors, nurses, psychologists, social workers and others. The patient also receives the medicines and other products associated with his treatment at home, and these are applied by the professional who makes the visit. Due to its particular characteristics, this type of care presents problems and challenges characteristic of both the ealth area (monitoring and evolution of the patient’s condition) and administrative (stock control and estimation of associated costs). For this reason, it was proposed that Beira Cama consists of a Web-mobile system that assists in the control of the workday of the professionals, monitoring their location and procedures performed by them through an application installed in a tablet that functions as a " digital prescription "containing the schedules, items and description of the medical procedure to be performed, where at the end of the same the professional informs if the same was performed or not. The web front is an administrative part where the professionals, products, patients and procedure are registered, as well as it is possible to carry out the monitoring of the costs associated with the operations as well as the monitoring of the employee and patients. At the end of the stage we were able to elaborate a MVP Minimum Viable Product that contains everything that was requested by the customer and ready to start being validated commercially.