TCC - Bacharelado em Ciência da Computação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/415
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11 resultados
Resultados da Pesquisa
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 Uma metodologia para a avaliação de desempenho e custos do treinamento de redes neurais em ambientes de nuvem(2024-03-07) Moura Filho, Cláudio Márcio de Araújo; Sousa, Érica Teixeira Gomes de; http://lattes.cnpq.br/9899077867723655; http://lattes.cnpq.br/8143173691280119Deep neural networks are solutions to problems involving pattern recognition and several works try to find ways to optimize the performance of these networks. This optimization requires suitable hardware to be implemented, hardware that can be very expensive for small and medium-sized organizations. The objective of this work is to propose a methodology to evaluate the performance and cost of training neural networks, considering the factors that most impact training time and evaluate the total financial cost of the environment for this task. In this sense, it was observed that factors such as the size of the input image and the network architecture have a great impact on the training time metric and consequently on the total cost.Item Avaliação de métodos de imputação de valores ausentes para a predição de interações fármaco-proteína(2024-03-08) Santos, Victor Vidal dos; Nascimento, André Câmara Alves do; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/7999257997046465In the last decade, the study of pharmacological networks has received a lot of attention given its relevance drug discovery process. Many different approaches for predicting biological interactions have been proposed, especially in the area of multiple kernel learning (MKL). Such methods comprise integrative approaches that can handle heterogeneous data sources, but suffer from the missing data problem. Techniques to handle missing values in the base kernel matrices can be used, usually based on simple techniques, such as imputing zeroes, mean and median of the matrix. In this work, techniques for handling missing values were evaluated in the context of bipartite networks. Our analyzes showed that the, depending on the amount of missing data, k-NN and SVD technique performed much better than the other techniques, bringing encouraging results, while zero-fill showed the worst performance in relation to all other evaluated methods.Item Development of machine learning models for the prediction of dissolved oxygen in aquaculture 4.0(2021-02-24) Freitas, Fábio Alves de; Nóbrega, Obionor de Oliveira; Lins, Fernando Antonio Aires; http://lattes.cnpq.br/2475965771605110; http://lattes.cnpq.br/8576087238071129; http://lattes.cnpq.br/5725435192607619The world faces the problem of feeding a growing population, which will reach more than 9 billion people by 2050. Thus, there is a need to develop activities that promote food production, within the dimensions of sustainability (social, technicaleconomic, and environmental). In this context, IoT systems focused on aquaculture 4.0 stand out, which allows the cultivation of high productions per unit of volume, with low environmental impact. However, these systems need to be extremely controlled, requiring sensors to perform realtime readings of water metrics, with emphasis on the dissolved oxygen (DO) sensor, which plays an essential role in determining the quality and quantity of available habitat for the organisms present in the system. Even with this importance, this sensor is often not used, due to its high associated cost. As an alternative solution to this problem, machine learning models have been proposed to predict DO, using temperature and pH readings as inputs. Experiments were carried out comparing different data scaling techniques and the prediction performance in different seasons of the year and regression metrics were used to evaluate the implemented models. The results showed that the proposed LSTM model is capable of making OD predictions and being applied in IoT and aquaculture 4.0 systems.Item Impacto da latência na verificação e do atraso acentuado na rotulação para detecção de falhas de software(2023-04-25) Sgotti, Lucas Cordeiro; Cabral, George Gomes; http://lattes.cnpq.br/8227256452129177; http://lattes.cnpq.br/6243434551046357Software Defect Prediction is an activity inherent to software development and it requires a high amount of human effort. This activity is often not prioritized in order toreduce the project's expenses. Just-in-Time Software Defect Prediction (JIT-SDP) is one of the approaches used for predicting software defects in order to automatically identify, through machine learning methods, software artifacts likely to contain defects based on historical data. However, most approaches assume that the characteristics of the problem remain the same over time, but the imbalance between classes is a problem that evolves over time as new training examples arrive, for example. Analyzing the problem in an online environment means that, among other things, there is an intrinsic chronological aspect to be considered which, in turn, brings with it some issues, among them verification latency, which refers to the fact that training example labels can arrive much later than their characteristics. This work aims to investigate the impact of verification latency on the problem of detecting defects in software, as well as the performance of these methods according to the degree of verification latency in examples of the defect inducing class.Item Teacher-Student aplicado a diferentes modelos de recompensa do ambiente Lunar Lander(2021-07-20) Silva, Kenedy Felipe dos Santos da; Sampaio, Pablo Azevedo; http://lattes.cnpq.br/8865836949700771; http://lattes.cnpq.br/7479192156880225The techniques studied related to learning by reinforcement are becoming more and more common in real world challenges, but one challenge is to reduce the learning time. Currently, the learning time and/or amount of interactions performed by the reinforcement learning agent can result in high costs in applications, as the training of models can consume a lot of time, requiring many interactions between the agent and the task environment. This work seeks to improve learning using a new combination of techniques, the Teacher-Student technique with Reward Shaping. The Teacher-Student technique aims to choose among a set of similar tasks that train for a main task, according to the student’s learning. The Reward Shaping technique, altering the reward to try to accelerate learning, provides more frequent feedback on appropriate behaviors, that is, reports rewards more often. We adapted Teacher-Student algorithms for this combination of techniques, and used the Lunar Lander environment as a case study, using four reward models designed in (ALBUQUERQUE, 2021) for this environment. Experiments were performed running different trainings to compare this approach with training only on the original Lunar Lander (no rewards change), and with the results obtained in (ALBUQUERQUE, 2021) by adopting each of the rewards models individually. The combination of Teacher-Student techniques with Reward Shaping contributed to a new experience in the reinforcement learning area, managing to accelerate the agent’s learning, considering the duration of 600 thousand training steps, reaching the target performance in 2 out of 5 proposals, in addition to of being able to learn better than the original Lunar Lander approach with PPO algorithm.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 Inferência automática de nível de dificuldade de receitas culinárias usando técnicas de processamento de linguagem natural(2020-12-21) Britto, Larissa Feliciana da Silva; Pacífico, Luciano Demétrio Santos; Ludermir, Teresa Bernarda; http://lattes.cnpq.br/6321179168854922; http://lattes.cnpq.br/9521600706234665; http://lattes.cnpq.br/5058497100007411In this work, a tool for inferring the degree of difficulty of cooking recipes will be proposed. The inference will be made by the textual classification of the recipe preparation methods. The tool will be a fundamental piece to the development of a contextaware contentbased cooking recipe recommendation system. Some of the main classifiers in Text Classification literature will be adopted, in addition to different feature extraction methods. An experimental evaluation is performed, in order to select the best approaches to compose the system.Item Tratamento de Kernels incompletos em redes bipartidas na predição de interações em redes biológicas(2020-10-30) Bastos, Tássia Laís Barros; Nascimento, André Câmara Alves do; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/2065961687962702In the last decade, the study of pharmacological networks has received a lot of attention given its relevance to the production of new drugs. The studies were made possible by the large volume of biological data generated, making it possible to understand and extract knowledge from them. However, although interesting, this is a process that brings with it some barriers in terms of viability, particularly when the data appear heterogeneously and contain missing information. Many different approaches for predicting biological interactions have been proposed, especially in the area of multiple kernel learning (Multiple Kernel Learning (MKL). The use of MKL methods in biological data is also compromised by the heterogeneity of data sources, but associated with the methods, techniques for complementing missing values in the base kernel matrices can be used, this filling process is usually done with simple techniques, such as imputing zeroes, mean and median of the matrix. In this work, techniques for handling false values were evaluated in the context of bipartite networks to solve the limitations related to the heterogeneity of the data. We used three single value imputation techniques (mean, median and zero) and a more complex predictive imputation technique (SVD). All the aforementioned techniques have already been used for matrix completeness in the context of unipartite networks. Our analyzes showed that the SVD technique performed much better than the other techniques in evaluative metrics, bringing encouraging results for the use of the technique in models that use bipartite networks. The average and median techniques showed similar performances, but lower than the SVD. And filling with zero showed the worst performance in relation to all other applied techniques.