Bacharelado em Sistemas de Informação (Sede)

URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12

Siglas das Coleções:

APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso

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Agora exibindo 1 - 11 de 11

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/1399502815770584
Sentiment 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.
Como o uso de Play Feature Delivery no Android pode ajudar na sustentabilidade digital
(2023-09) Claudino, Yasmmin Maria Monteiro; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/0549149216731460
With the increasing access to the internet via mobile devices among the less privileged classes in Brazil, digital sustainability becomes of growing importance. The aim of this study is to assess the impact of the Play Feature Delivery technology on mobile data consumption on Android devices. A parametric t-test was applied to evaluate significant differences between the averages of data amounts spent in Megabytes when downloading two applications. The result corresponded to a t-value of approximately 65.55 and the rejection of the null hypothesis. This finding not only underscores the technical importance for Android developers but also highlights its relevance in showing an improvement in mobile data usage, especially in an era where the democratization of access to information is vital. The research reinforces the idea that society should adapt to digital resources, optimizing data usage. To reach these conclusions, two mobile applications focused on first aid guidelines were developed and analyzed. The main advantage observed was the reduction in mobile data consumption, validating the efficacy of Play Feature Delivery compared to conventional applications.
Construção de pipelines de dados sobre obras públicas em Pernambuco: abordagem prática com o Apache Airflow
(2023-09-21) Silva, Henrique César José da; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584
This study presents a practical approach to building data pipelines focused on collecting, transforming, and storing information related to public works in the state of Pernambuco. The central objective is to develop efficient and automated workflows for extracting data from public transparency portals and subsequently consolidating this information. Based on Data Engineering technologies, the Apache Airflow framework was chosen to orchestrate the processes, enabling the scheduling and monitoring of these workflows.
Detecção de doença cardiovascular ou diabetes utilizando machine learning
(2024-03-07) Santos, Daniel Ramos Correia dos; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584
Cardiovascular diseases and diabetes represent significant challenges for public health, requiring effective diagnostic and prevention approaches. This work proposes an approach based on machine learning models to support these processes. Using a database from the IBGE national health survey, the study investigated how different variables affect the detection of these diseases. Using algorithms such as Random Forest, XGBoost and SVM, predictive models were developed. The results demonstrated an accuracy of 71.96% for the Random Forest algorithm in classifying patients with cardiovascular diseases and 72.26% in classifying patients with diabetes. Analysis of the most influential variables was also carried out using the SHAP method, which revealed some insights into the data.
Estudo comparativo de algoritmos de classificação supervisionada para classificação de polaridade em análise de sentimentos
(2019) Albuquerque, Rotsen Diego Rodrigues de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6441716676783585
The huge increase of data on the Internet, it is a rich source for public opinion assessment of a specific subject. Consequently, the number of opinions available makes decision-making impossible if it is necessary to read and analyze all opinions. Since the use of Machine Learning has been widely used, I will present a comparative study of two algorithms for classifying movie comments using techniques of natural language processing and Sentiment Analysis. The data obtained were obtained manually where through the competition site called Kaggle where we have about 50,000 comments on various films. The purpose of this study is also to use the concepts of data science and Machine Learning, natural language processing and sentiment analysis to add more information about the entertainment and film industry. That is why these algorithms were created so that it is possible to show the results for this domain in the of movies comments registered in one big site/platform of movie industry, the famous IMDB. After training and testing, the machine had an accuracy of 86 % on predicting sentiments on commented text from movies.
Sistema integrado para registro de aulas e análise de frequência dos alunos do Projeto Conecta Vidas do Centro Tecnológico da Associação Conexão Social
(2024-10-09) Santos, Djair Batista dos; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584
The Social Connection Association (ACS), founded in 2005 in Lagoa de Itaenga, Pernambuco, has the mission of guaranteeing the rights of people in situations of social vulnerability through education and inclusion. The Conecta Vidas Project aims to promote digital literacy and physical activities for the elderly, contributing to their active aging and strengthening community bonds. However, the registration of attendance and the documentation of activities face significant challenges, such as the logistics of real-time records and the security of sensitive beneficiary data. This report presents a proposal for a responsive Integrated System for Class Registration and Attendance Analysis, developed with React, Node.js and MySQL technologies. The proposed solution seeks to optimize attendance registration, facilitate the secure storage of photos and simplify the flow of information for coordination. This is intended to improve the efficiency of educators, ensure data protection, and provide detailed and accurate reporting to funders. The system aims not only at the continuity and success of the project, but also at the promotion of a more transparent and effective management, enhancing the benefits offered by ACS.
Suporte à decisão multicritério em aplicativos de saúde sob demanda
(2019) Pereira, Gustavo Magalhães; Albuquerque Júnior, Gabriel Alves de; Falcão, Taciana Pontual da Rocha; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6456769669695121
Health on demand applications have the main purpose of finding a doctor and take him to your home to provide home care for those who have limited mobility and seek a more convenient medical service, who do not want to face waiting lines and who wish to avoid to go to a hospital to treat basic illnesses. The technological advance has transformed the way traditional services are offered on demand, which is increasingly popular in Brazil. The Federal Council of Medicine (CRM), knowing the impact of technological advances in the practice of medicine, published a resolution No. 2178/2017, which seeks to regulate the operation of applications that offer medical consultation at home. According to the resolution, all applications that offer this service are required to provide a list of physicians available to the patient to choose the best doctor to take care of their case, but the applications do not offer patient assistance in the decision and is in need of a computational solution. In this work was carried out the planning and development of a recommendation system using the methodology to support multi-criteria decision analysis. It was used as a case study the application Clinio, a product of health on demand developed by Epitrack. The solution applied to recommend the best physicians has the purpose of helping the users of the application in choosing the professional that best fits their needs and preferences. To do so, it was used recommendation algorithms to select doctors based on symptoms and geolocation and the Analytical Hierarchical Process (AHP), based on criteria to classify them such as the value of the consultation, the distance between the doctor and the patient, and the age of the physician. The system was implemented using a database of 143 doctors from Pernambuco who work in 10 clinical cases. Through the tests performed it was observed in the recommendation obtained by the users that the system assists in the process of choosing the best professional for a case through the preferences definitions.
Uso da ciência de dados para estudo de falhas e fraudes dos abastecimentos de postos de gasolina
(2019-12-19) Arruda, Luiz Felipe Ribeiro de; Albuquerque Júnior, Gabriel Alves de; Roullier, Ana; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/1825682578554550
Uso de análise de sobrevivência como ferramenta na obtenção de indicadores de permanência no ensino superior
(2024-03-06) Silva, Lhaíslla Eduarda Cavalcanti Rodrigues da; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/7477398253297436
This paper proposes an integrated approach of statistical techniques, business intelligence and data science concepts to create a survival analysis model aimed at understanding student retention in higher education. Using computer science courses as a case study, different groups are compared in order to calculate the probability of students staying until the end of the course. The work makes use of the SABIA platform to support data-driven management, highlighting the importance of technological tools in academic analysis. The results show patterns between courses in the same area when considering dropout as an event of interest, with steeper drops in the initial periods, especially at critical moments, such as the second semester, which show lower probabilities of permanence compared to the first semester, as well as the Other entry modality, which in the final periods reflects the occurrence of dropout, highlighting the importance of personalised interventions to prevent dropout and waste of resources, contributing to more efficient and effective management of higher education institutions.
Uso de business intelligence como ferramenta de apoio em previsões de apostas de jogos de futebol
(2021-12-16) Domingues, Marcela Soares; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6238090449898774
The number of Brazilians that currently make investments on online sports betting, mainly related to football teams, are huge. This practice is allowed in Brazil since 2018, according to the law 3.756/18. With this permission granted, added to the Brazilian passion for football games, this is an investment area that has been growing fast since then, reaching high levels of financial movements, making people to call on this practice as guaranty of extra or main source of incomes. To make an investment in an specific bet, the ideal is that you have some idea of the risks you are taking. Betting involves losses and sometimes the investments are worthless. To know that, is necessary to take into account some variables as historical data from football teams and players. The use of Business Intelligence, that is already used for some teams to study the behaviour of others, is also of great value when it comes to analyze which bets are worth to make an invesment. Through BI concepts, this paper presents the development of a dashboard containing graphs and reports with the intention to help on the bet analysis. Besides that, it also presents a predict model to find out the amount of goals in an specific match and the calculation of winning probabilities of each team in a given match.
Uso de Machine Learning para identificação de solicitação de teste de confirmação em projeto de teste de software
(2022-06-06) Santos, Victor Leuthier dos; Monteiro, Cleviton Vinicius Fonsêca; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/8817589533156593