TCC - Bacharelado em Ciência da Computação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/415
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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 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 Prevendo a evasão escolar em uma instituição de ensino técnico utilizando mineração de dados educacionais(2021-07-16) Lemos, Ítalo Vinícius do Rego; Nascimento, André Câmara Alves do; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/9146227756985212Dropping out of school is one of the main problems that occur in education and has become a very frequent reality within public or private educational institutions, resulting in immeasurable consequences both in the life of the student who fails to attend school and for society as a whole. Because it is a phenomenon that worries education professionals a lot, if it is necessary to reverse it, so these professionals need resources that are efficient in demonstrating knowledge inside and outside the teaching environment and outlining strategies to deal with such a scenario. Being able to predict a possible dropout benefits both the student and institutions. Based on this, a methodology that has proven to be skillful in combating school dropouts and capable of providing knowledge to the educational institution is Educational Data Mining. Based on this, this work aimed to apply Data Mining and Machine Learning techniques to predict possible dropout cases before the student enters the educational institution. Through social and economic indicators of the student and his family, he is classified as a potential evader or not. This study adopted a real database from a Brazilian public education institution, with data from candidates who competed in its admission process (entrance exam) for a place in technical education. During the research, 3 classification models were used Decision Tree, Random Forest e XGBoost with the XGBoost algorithm achieving a 74% hit rate in predicting evaders, being superior to the others but still presenting a high number of students classified as not dropouts but who actually dropped out. Given these results, we conclude that more indicators are needed to satisfactorily detect the possible candidate who will drop out.Item Coh-Metrix PT-BR: uma API web de análise textual para à educação(2021-03-02) Salhab, Raissa Camelo; Mello, Rafael Ferreira Leite de; http://lattes.cnpq.br/6190254569597745; http://lattes.cnpq.br/6761163457130594CohMetrix is a computational system that provides different measures of textual analysis, including legibility, coherence and textual cohesion. These measures allow a more indepth analysis of different types of educational texts such as essays, answers to open questions and messages in educational forums. This paper describes the features of a prototype, which encompass a website and an API, of a Brazilian Portuguese version of CohMetrix measures.Item Recomendação e geração de receitas baseada na substituição de ingredientes(2020-12-21) Oliveira, Emília Galdino de; Pacífico, Luciano Demétrio Santos; Ludermir, Teresa Bernarda; http://lattes.cnpq.br/6321179168854922; http://lattes.cnpq.br/9521600706234665; http://lattes.cnpq.br/6278486720525640Nowadays, even with the increasing number of recipe sharing websites and systems, users may have difficulty to search specific dishes through the massive amount of data contained in such repositories. Also, finding recipes which best fit a handy set of ingredients, while at the same time contemplate some user wishes and restrictions, may become a very time consuming or even impossible task. In this work, we propose a new recipe recommendation and generation system, based on the substitution of recipe ingredients and a datadriven approach, in an attempt to help users finding a recipe that contemplates both their desires and food restrictions, avoiding food wastes.Item Graph Embeddings para Node Classification em representação baseada em grafos de frases em linguagem natural(2019) Silva, João Marcos Nascimento da; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340; http://lattes.cnpq.br/5276914899067852Due to the large amount of works developed in the biomedical field and the availability ofhuge databases on biomedical entities, including proteins, genes and viruses, it comesthe need to be able to automatically index such human knowledge bases.Such need has led to the development and computational tools to assist the researcherin the recovery of specific information involving certain proteins and their relations. Inthis context, two of the main problems in the biomedical area involving techniques of Text Mining most investigated are the Named Entity Recognition (NER) and RelationExtraction.This work focuses on the first problem that serves as a basis for the second, i.e., first wehave to identify and classify the entities and then, with the identified/classified entities,identify the existing relations between them, if any. The approach adopted in this paperis based on the recent techniques of supervised/non-supervised learning of deep neural networks, or Deep Learning (DL). In particular, the problem of NER is investigated usingrecent techniques of dense feature representation using DL.At first, the sentences from a biomedical corpus are represented as graphs thanks tothe generation of annotations (metadata) generated automatically by natural language processing tools, such as tokenization, syntactic parsing, etc. These graphs are thenimported into a graph-based database so that various queries submitted to this data base can be optimized in order to extract both lexical and syntactic attributes (or features) ofthe entities (or nodes) present in the graphs. The information generated in the previousstep is used as input Deep Learning-based algorithms called Graph Embedding (GE)that map the representation of graph nodes (entity) in a dense vector representation(vector of real numbers) that has several properties of interest for this search. Finally,such dense representation of features) are employed as input for supervised machine learning algorithms.This work presents an experimental study where some of the existent algorithms of GEare compared, along with several types of sentence representation based on graphs,and their impacts on the task of entity classification (NER), or node classification. Theexperimental results are promising, reaching more than 90% accuracy in the best casesItem Abordagem híbrida e independente de domínio para extração de aspectos na análise de sentimentos(2018) Lins, André Lucas Machado; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340; http://lattes.cnpq.br/3233947254235611Opinions are central in most of the human activities and are keys of influence to our behaviors. Our beliefs, perception of reality and our choices are in a considerable degree, influenced by how people see and evaluate the world. In view of this statement theSentimentAnalysis(SA)hasbeengrowingconstantly,thepossibilityofunderstand people’sfeelingsandopinionsaboutcertainsubjectsgetseveryoneexcited.Sentiment Analysisisthecomputationalstudyofpeople’sopinions,attitudesandemotionsabout some entity. The literature about Sentiment Analysis is pretty wide, having too many ways of execute such tasks. A variation of SA called Aspect based Sentiment Analysis (ABSA) has been receiving researchers attention. In this approach feelings are identified in relation to sentence aspects, in order to discern those that are treated in eachsentenceordocument.ABSAisdividedinthreemajortaskswhicharetheextraction,classificationandaggregationoftheaspect,havingaspectextractionasthemost complextask.There’sseveralapproachestosolvetheaspectextractiontask,although manyoftheseapproachesaredomaindependent,makingdifficulttoreplicatetheseapproaches to domains that does not have the same features. Therefore, this work aims topurposeadomainindependenthybridmethodtoaspectsextraction,thatconsistsin fourmajorsteps.Thefirstoneidentifyallthepossibleaspectsoutofsemanticrulesfor eachsentence.Afterthisstep,willbegeneratedalexicalofallthesentenceshavingthe aspectsandmostrelevantfeelings.Inthefollowstepismadethepruningofpossibleaspectsusingsemanticrulesthroughthelexicalofaspectsandfeelingsmadepreviously. Lastly,ismadeaselectionamongtheremainingaspectsbyadynamicthreshold.This purpose was evaluated in the Semeval’s dataset, containing opinions about several aspects related to restaurants and laptops, using the most adopted evaluation metrics in literature. The experimental results imply that the proposed method is competitive when it’s compared to many other methods dependents and independents of state of art domain.