TCC - Bacharelado em Sistemas da Informação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/427
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Resultados da Pesquisa
Item Técnicas preditivas para auxílio no diagnóstico de melanomas via imagens(2024-10-02) Silva Júnior, José Carlos Monte; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964Skin cancer is the most common type of cancer worldwide, divided into two main types: melanoma and non-melanoma. Although rarer, melanoma is the most lethal due to its potential to cause metastasis. Non-invasive methods, such as dermoscopy and the ABCDE rule, have been used to avoid unnecessary surgical procedures and have helped in the identification of lesions, contributing to faster diagnoses. With advances in technology, Artificial Intelligence (AI) has gained prominence, proving to be a promising solution for medical data analysis, especially with the use of Convolutional Neural Networks (CNNs), which can recognize patterns in dermoscopic images and help classify lesions as melanoma or non-melanoma in an automated manner. This project proposes an ensemble of classifiers based on Convolutional Neural Networks to classify dermoscopic images as melanoma or non-melanoma, comparing its performance with validated architectures, such as AlexNet and VGG-16, using Transfer Learning techniques The analyses of Precision, Recall, and F1 Score showed that the ensemble of Convolutional Neural Networks outperformed the models using Transfer Learning techniques, with AlexNet showing better performance than VGG-16. The ensemble of Convolutional Neural Networks demonstrated a greater generalization capability, proving to be promising in capturing relevant features from the images, revealing potential for medical applications, although it still needs refinement to meet clinical standards.Item Técnicas de comitês para a estimação de esforço na correção de software(2019-12-10) Guimarães, Ariana Lima; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2605671850587343A well-defined planning of a software project, since the early stages, is indispensable to its success, whether the development refers to product’s creation or maintenance. Accordingly to the software life cycle, maintenance is continuously executed after the product’s building and delivery, in parallel to the tests execution by engineers and/or users. In this stage, User Stories and Issue Reports are the first documents to be presented. These documents describe, in natural language, business requirements, error scenarios found, expected corrections and enhancements for the system. Its objectives are, among other things, ranking the activities needed to be accomplish during the project. Therefore, in line with the available resources – human, financial and temporal -, it is possible to estimate the effort that will be necessary in the activities development and generate essential information for an effective and efficient planning. As these documents are written in natural texts, it raises the opportunity to use Natural Language Processing and Machine Learning (ML) to predict software effort. In practice, in the daily life of software factories, it is common to use experts’ and project staff’s opinion to judge the effort required by an activity during Planning Poker sessions. Usually, in this technique, the effort is measured in Story Points, which follow Fibonacci sequence. But this planning model requires the scaling of more resources to be executed. The application of ML causes in a system, after the learning phase, the ability to seize the team experience and replicate it quickly and automatically to estimate the activities effort. Thus, this work covers the ML field, proposing a PV-DM Ensemble approach to extract features of Issue Reports to estimate Story Points, the effort indicator. Compared to the two other approaches of BoW and simple PV-DM, the proposed technique has presented good results, about 80% of f-measure, in a supervised learning SVM classifier. The experiments results proved to be a starting point for further study of PV-DM Ensemble approach and its improvement.