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 Uso de machine learning para previsão de valores de apartamentos no município do Recife(2023-09-12) Silva, Thiago César de Miranda; Monteiro, Cleviton Vinicius Fonsêca; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/8285740572952516The COVID-19 pandemic has brought with it a series of economic effects and transformations related to behavior and the way people live, which, in turn, have had repercussions on property prices and real estate demand. In this context, property price forecasting assumes an extremely important role, contributing to more informed decisions, mitigating risks, and promoting greater transparency in the real estate sector. The implementation of automation in price forecasting further enhances this dynamic, significantly improving accuracy, efficiency, and reliability of predictions, while providing adaptability to economic fluctuations with greater agility. Utilizing listings available on OLX, a georeferenced database was created to generate a residential apartment price prediction model in Recife, using machine learning models in AutoML. This tool automates the development of machine learning models, enabling rapid experimentation and a focus on problem-solving. The work indicates that the poor geographical distribution of the data has biased the results of the models. Furthermore, it was concluded that the data found on online buying and selling platforms are insufficient for generating a machine learning model that achieves an acceptable level of accuracy in Recife, mainly because transaction values for the properties are not provided, only the advertised prices. However, this current work provides significant contributions to the advancement of research related to automation in real estate price prediction.Item Análise de Sentimos de Tweets Relacionados ao Uso de Máscara Durante a Pandemia da Covid-19 no Brasil(2022-10-07) Oliveira, Felipe de Araújo Morais Vilar; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972The world has recently gone through a global crisis. The COVID-19 pandemic began in a Chinese city called Wuhan in mid-December 2019 and spread across the world, infecting more than 596 million people and causing about 6.68 million fatalities. As the COVID-19 virus has much of its proliferation and contagion through the airways, experts and scholars in the health area recommended that the entire population wear masks in an attempt to stop the number of cases by creating a physical barrier to try to contain the respiratory droplets that serve as a means of spreading the virus. The use of masks in Brazil was adopted at the beginning of April of the year 2020, but its mandatory only started around the end of May of the same year. However, the misinformation about the use of the face mask generated great controversy, doubts and discomfort among the Brazilian population. This work aims to analyze the feeling of the Brazilian population regarding the use of masks as PPE (Personal Protective Equipment) through posts (tweets) taken from Twitter. The results reveal that an average of 89.3% of the tweets related to face masks were neutral. Most of these neutral tweets show the Brazilian population’s discomfort in using masks, but at the same time accepting the need to use them in an attempt to stop the spread of COVID-19.Item Estudo de técnicas preditivas para o auxílio a gestores na pandemia de COVID-19(2022-05-27) França, Eliana Maria Silva de; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2782168150783950The main objective of this work is to propose an alternative to exploratory statistical surveys, to support the decision-making of managers, during the confrontation of the COVID-19 pandemic. To this end, a methodology was created, using machine learning to provide a new tool for predicting deaths caused by COVID-19, from open data that contain sanitary, demographic and population characteristics. In such a way that, from this study, an artificial intelligence model can be developed capable of helping to face the COVID-19 pandemic. Of the 3 artificial intelligence algorithms used (Decision Tree, Support Vector Machine and Multilayer Perceptron), the model based on Support Vector Machine showed the best performance, because it has the lowest Mean Absolute Error, a metric used to measure the quality of regression-based artificial intelligence models.