01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
Navegar
20 resultados
Resultados da Pesquisa
Item Implementação de um sistema mobile colaborativo para acompanhamento do quadro de pacientes com esclerose múltipla por meio de análise de sentimento(2024-10-02) Araujo, Paula Priscila da Cruz; Gouveia, Roberta Macêdo Marques; Tschá, Elizabeth Regina; http://lattes.cnpq.br/9598413463162759; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/0280090820230057The study aims to develop a mobile system to facilitate the monitoring of patients with Multiple Sclerosis (MS), based on the Human-Centered Design (HCD) Toolkit to meet patient needs. The app allows patients to record and track emotions, symptoms, and treatments, offering monthly reports and personalized alerts. For sentiment analysis, the machine learning algorithms XGBoost and Naive Bayes were used, with XGBoost showing better performance, achieving 87.56% accuracy and an F1-Score of 0.876, while Naive Bayes obtained 62.25% accuracy and an F1-Score of 0.524. The results indicate the tool’s effectiveness in emotional and medical monitoring, contributing to an improved quality of life.Item Análise de sentimentos em reviews de jogos digitais da Plataforma Steam(2024-09-26) Albuquerque, Júlia de Melo; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584Sentiment analysis is an area that investigates the emotional expressions of human language, aiming to understand the underlying needs and opinions expressed in texts. Its complexity lies in the ability to discern not only the textual content but also the implicit emotional matrices. With technological advancements, the ease of publicly expressing opinions is disseminated through various means, with online gaming being a sector that attracts numerous player posts about various available titles. However, this diversity of audiences and topics makes it challenging to understand the expressed sentiment that pervades this universe. The aim of this study is to apply sentiment analysis techniques to digital game reviews, adopting an approach focused on supervised machine learning algorithms and pre-polarized libraries, in order to identify the best classification path capable of discerning the sentiments expressed by users in the reviews. This operation considers an approach with all opinions and another focused on each game’s specific genre. This analysis was conducted by exploring data from an online game distribution company (Steam), followed by data preparation due to the peculiarities present in the records. The results reveal that machine learning models outperform traditional approaches, such as using the VADER library, showing a higher precision by approximately 10% in captures. A difference of 20% more was observed in metrics such as recall and F1-score. This study represents an analytical contribution to the field of sentiment analysis, highlighting the model’s ability to deal with the complexity of human language.Item Sistema de suporte à criação de modelos de classificação para a previsão de evasão no ensino superior(2024-03-08) Costa, Tarcísio Barbosa da; Alencar, Andrêza Leite de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6060587704569605; http://lattes.cnpq.br/6560255346406064Student dropout is one of the greatest challenges faced by university degree institutions. In order to mitigate it, those institutions develop moitoring and analysis tools regarding this phenomenon. One of many existing methodologies to do so is the recognition of student characteristics that leads to dropout, and ond of many existing tools is SABIA: a virtual dashboard responsible for supporting evidence-based management allied to concepts like Learning/Academic Analytics and Business Intelligence. This work expands SABIA through a new page able to create user-customizable supervisioned learning models, offering feature analysis from students and predicting their final status based on those features. Information obtained through those models enables the recognition of risk features on student profiles and assists managers on providin guidelines for applying countermeasures against dropout.Item Análise da evasão no ensino superior: predição e prevenção por meio da mineração de dados educacionais(2024-03-05) Ferreira, Rodolfo André Barbosa; Mello, Rafael Ferreira Leite de; http://lattes.cnpq.br/6190254569597745; http://lattes.cnpq.br/2982020271806247Considering that dropout occurs due to abandonment, transfer, or withdrawal from the course; when the student disengages from the institution they are enrolled in or when the student definitively abandons or does not complete higher education, this article seeks to identify methods and automated techniques to assist managers in preventing dropout cases through predictions. To conduct the study, Educational Data Mining (EDM) was used, which applies data mining techniques such as database, statistics, and machine learning in education. Data from 5144 students with characteristics related to course, semester, and demographics were used from the database provided by the Academic Information and Management System (SIGA) of the Federal Rural University of Pernambuco (UFRPE) for the courses of Animal Science, Fisheries Engineering, and Agronomy. The data, except for those containing personal, restricted, and sensitive information, were separated into Academic Characteristics per Semester, General Academic Characteristics, Course-related, Demographic, and Target Characteristics. The study employs the LSTM machine learning algorithm and the SGD and Adam optimizers, exploring different values for the parameters of learning rate, momentum, batch size, and number of epochs.Item An implementation of a mathematical-computational method for the detection and treatment of financial outliers in higher education(2023-09-06) Freitas, Nathan Cavalcante; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1613649528791400The Higher Education Census occurs annually, collecting data from public and private Higher Educational Institutions (HEI) in Brazil. Different factors can lead to anomalies or outliers in some of these collected data. This work proposes a mathematical-computational method to detect and treat atypical HEI’s financial values. Both univariate and bivariate analysis to that end. We analyzed the expenses and incomes of HEI in the census from 2016 to 2019. This analysis revealed that 204 out of 2,224 HEI, approximately 10%, reported some atypical data.Item Análise dos Impactos da Gestão do Tempo no Desempenho Acadêmico Através da Mineração de Dados Educacionais(2023-03-29) Nascimento, Pricylla Santos Cavalcante do; Rodrigues, Rodrigo Lins; http://lattes.cnpq.br/5512849006877767; http://lattes.cnpq.br/2042576149331188With technological advances, new challenges were born. Amongst them is the problem of identifying factors that corroborate with the good academic performance of students of distance learning courses. This work aims to analyze the impacts of time management on the academic performance of the student. For this, the K-means technique was used to group students in relation to their academic performance, a neural network was used to classify these groups according to the time management variables, and the SHAP method was used to interpret the classes obtained in an efficient way. The construction of this research uses data from distance learning courses extracted from the moodle platform of a public university in the state of Pernambuco. As a conclusion, it was possible to observe which characteristics of time management impact the student's academic performance positively.Item Análise de sentimentos de tweets relacionados a vacinas antes e durante a pandemia da COVID-19 no Brasil(2023-03-01) Silva, Íkaro Alef de Lima; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/7938306473921402In early 2020, the COVID-19 disease spread rapidly around the world and one of the ways to fight it is the vaccine. Governments faced problems with fake news and anti-vaccination groups. Thus, it is necessary to understand the feelings of the population in order to propose efficient public policies. This article describes a sentiment analysis on vaccine-related tweets in Brazil from June 2020 to June 2021. The results revealed peaks in total tweets in January and May 2021, the predominance of positive tweets, and feelings of confidence, fear, submission and sadness. They are also associated with former President Jair Bolsonaro. The negative polarity was the least common, showing that the Brazilian population was receptive to vaccines.Item Técnica de clusterização aplicada à análise de perfis socioeconômicos de estudantes concluintes de cursos de computação(2022-07-03) Souza, Clarissa Cordeiro de; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1046530929912898The different social and economic classes of undergraduate students can impact the course of academic training and the permanence of such students in Brazilian higher education institutions. This course conclusion work applied a data mining technique called K-means clustering to the microdata of the 2017 National Student Performance Exam (ENADE), an exam applied by the National Institute of Educational Studies and Research Anísio Teixeira (Inep), with the aim of analyzing the contexts that separate graduates from the various computer courses, whether bachelor’s or licentiate, using socioeconomic data. The results pointed to four large groups of students and, based on their analysis, it is possible to list a profile of a graduate student of computing in the year analyzed, since the clusters have several characteristics in common, such as: most students are of the sex male, single, white, opted for the face-to-face modality, attended high school in public schools, among others. However, some characteristics were found in specific groups, for example there is a group of graduates who are from full-time public institutions.Item Análise de sentimentos em Tweets relacionados ao desmatamento da Floresta Amazônica(2021-12-17) Silva, Vinicius José Paes e; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/7437953784606274The Amazon Forest is being devastated at the fastest pace in recent years. In 2021, the Amazon rainforest registers the largest accumulation of deforestation in 5 years, increasing from 13 thousand km2 between August 2020 and July 2021. An increase of 22% compared to the same period in the previous year, the highest number since 2006. Although many works address the issue of deforestation, none of them focus on analyzing the sentiments of the Brazilian population regarding the issue. This work presents an analysis of the sentiments of the Brazilian population related to the deforestation of the Amazon rainforest through the text mining of Twitter and aims to understand how Brazilian users opine and dialogue about the deforestation of the Amazon rainforest. The results reveal that Brazilian users tend to react to events related to deforestation in the Amazon forest on Twiter and that most users have a negative sentiment about the topic, reaching peaks of approximately 60% of tweets in a given time.Item Raspagem de Dados Jurídicos Utilizando Scrapy(2021-12-20) Barbosa, Jadiel Eudes Mendonça; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/8044959053132773Web scraping is a computational technique that uses a program to extract data that are hidden in web pages. In this way, this academic work aims to use how web scraping techniques to extract data from legal processes from the websites of the courts in order to help contracting companies to take strategic decisions with their legal departments.