DSpace Communidade:
http://repositorio.ufc.br/handle/riufc/23978
2024-03-29T07:06:25ZAplicação do método de Monte Carlo na avaliação de empresas: desenvolvimento de um modelo de simulação em Python
http://repositorio.ufc.br/handle/riufc/76430
Título: Aplicação do método de Monte Carlo na avaliação de empresas: desenvolvimento de um modelo de simulação em Python
Autor(es): Silva, Lucas Rodrigues da
Abstract: Valuation, crucial for investors and managers, highlights the essential role of the Monte Carlo Method (MMC) in enhancing risk analysis. Originating in nuclear physics, MMC employs stochastic sampling to simulate complex scenarios, playing a critical role in evaluating companies in volatile economic environments. This work aims to develop a simulation model in Python using MMC to calculate the Discounted Cash Flow (DCF), using real data from publicly traded companies. Additionally, it explores the theoretical foundations of MMC and analyzes key variables that influence DCF calculations. Implementation was carried out on Google Colab, employing 10, 100, 1000, and 5000 simulations to analyze the results, with a focus on Grendene company data. The results demonstrate the effectiveness of the proposed model, validating it as a valuable tool for strategic decision-making related to company valuation.
Tipo: TCC2023-01-01T00:00:00ZUtilizando reactjs para desenvolvimento de uma interface gráfica para o sistema web CEUA Sobral
http://repositorio.ufc.br/handle/riufc/76407
Título: Utilizando reactjs para desenvolvimento de uma interface gráfica para o sistema web CEUA Sobral
Autor(es): Silva, Francisco Vilmar Rodrigues da
Abstract: The process of transforming the capture and storage of data from the Commission on Ethics in the Use of Animals (CEUA) processes into a web project is extremely necessary to bring improvements to the team that manages, organizes and handles the documentation and requests that constantly appear. Requests require a concentration of documents and paperwork, which needs to be handled in a well-organized way to make the process less complex. With this in mind, it was proposed to develop an intuitive and easy-to-use interface, meeting users’ demands and feedback, so that it can be accessed in an organized way, having a perspective on the stages related to the development of research.
Tipo: TCC2023-01-01T00:00:00ZDetecção automática de defeitos em células fotovoltaicas através de redes neurais convolucionais
http://repositorio.ufc.br/handle/riufc/76400
Título: Detecção automática de defeitos em células fotovoltaicas através de redes neurais convolucionais
Autor(es): Serafim, Francilandio Lima
Abstract: The climate crisis observed in recent years, coupled with the depletion of fossil fuels, has made the adoption of alternative, sustainable and renewable energy sources imperative. In this scenario, solar energy has been employed as an energy source to meet the global demand for electric power. The observed increase in photovoltaic (PV) solar electricity generation brings demands
for methods for inspecting and maintaining PV systems, as their components are subject to various types of defects such as microcracks, shading, open circuits, short circuits, overheating, among others. For the detection of these defects, some techniques have been developed such as the analysis of the I-V curve and inspection of thermal images or Electroluminescence (EL) of
PV cells. The latter has been addressed in several works that seek to automate defect detection through computer vision techniques and machine learning. The present work follows this line of research, seeking to automate the detection of defects in monocrystalline PV cells through EL images. A three-step computational model is proposed here: pre-processing, feature extraction, and defect diagnosis. In pre-processing, a Bilateral Gaussian filter is applied to the images, to then obtain, during the feature extraction phase, a texture descriptor of the images by the Local Binary Pattern (LBP). Finally, the descriptions of the texture of the resulting images feed a Customized Convolutional Neural Network (CCNN), trained and tested in the task of classifying cells as defective or non-defective, in the defect diagnosis phase. The CCNN is the main contribution of the present work, being composed of a topology with a reduced number of parameters, when compared to other traditional networks. The performance obtained in cross-validation tests proved that the proposed convolutional network is competitive in relation to other approaches present in the literature. It was concluded that the model meets the needs of the problem of defect detection in PV cells, with accuracies of 94% and 86% in the classification of defective and non-defective cells, respectively.
Tipo: TCC2023-01-01T00:00:00ZAnálise da percepção sobre o viés reproduzido pelos algoritmos de reconhecimento facial
http://repositorio.ufc.br/handle/riufc/76386
Título: Análise da percepção sobre o viés reproduzido pelos algoritmos de reconhecimento facial
Autor(es): Martins, Antonio Eraldo Caetano
Abstract: The advancement in algorithm development enables the execution of tasks primarily performed by humans, easily replicable by Artificial Intelligence (AI) systems. Facial recognition systems, for instance, are adopted by companies and organizations to conduct studies with the purpose of automating tasks. In this process, problems arise concerning the results produced by these
systems. These problems can be caused by algorithmic bias. In light of this, this study conducts an analysis of the perception of bias in the comments extracted from two databases on the social media network Reddit, where users commented on posts with facial recognition as the main topic. The methodology consisted of extracting and analyzing sentiments from the comments using the VADER sentiment analyzer from the Python Natural Language Toolkit (NLTK) and obtaining the most frequent terms for subsequent analysis. Positive and negative sentiments were found in similar quantities, and the extraction of the most mentioned expressions revealed the relationship between rights violation and the sentiments identified
Tipo: TCC2023-01-01T00:00:00Z