Programa de Pós-Graduação em Engenharia Elétrica e de Computação
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Master Thesis Substituições de aminoácidos como representação de mutações em transformers para análise e classificação de variantes do SARS-CoV-2(Universidade Federal do Rio Grande do Norte, 2025-12-19) Silva, Alessandro Soares da; Fernandes, Marcelo Augusto Costa; https://orcid.org/0000-0001-7536-2506; http://lattes.cnpq.br/3475337353676349; http://lattes.cnpq.br/6391394214446756; Câmara, Gabriel Bezerra Motta; https://orcid.org/0000-0002-5736-0782; http://lattes.cnpq.br/3849103334728892; Terrematte, Patrick César Alves; https://orcid.org/0000-0002-0385-0030; http://lattes.cnpq.br/4283045850342312; Barbosa, Raquel de Melo; https://orcid.org/0000-0003-3798-5512; http://lattes.cnpq.br/8174411728088087The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the need for scalable and automatable computational methods for genomic surveillance in the face of the continuous emergence of variants of concern. In this context, this work proposes an innovative approach based on vector representations of amino acid substitutions, leveraging Transformer models for the analysis and classification of SARS-CoV-2 viral variants, with a focus on the Alpha, Beta, Gamma, Delta, and Omicron variants. Sequences of amino acid substitutions were processed into high-dimensional embeddings, which supported two complementary experiments. In the first, unsupervised techniques, such as Fuzzy C-Means clustering and t-SNE projection, were applied to reveal groupings consistent with known variants, as well as transitional regions and ambiguous samples. In the second, supervised learning models, including SVM, Random Forest, k-NN, and XGBoost, were evaluated for variant classification, with XGBoost achieving accuracy above 99% on an external dataset. The results demonstrate that representations derived exclusively from amino acid substitutions are capable of discriminating viral variants with high performance and capturing mutational signatures of biological relevance, without requiring genomic alignment. Thus, the proposed approach represents a scalable, flexible, adaptable, and purely computational alternative for automated pathogen genomic surveillance, with potential applications in public health scenarios.Doctoral Thesis Monitoramento e diagnóstico de falhas em motores de indução trifásicos utilizando rede neural NARX(Universidade Federal do Rio Grande do Norte, 2024-12-05) Araújo, Valbério Gonzaga de; Salazar, Andres Ortiz; http://lattes.cnpq.br/1343977279035189; Maitelli, André Laurindo; Ferreira, Jossana Maria de Souza; Paiva, José Álvaro de; Villanueva, Juan Moisés MauricioThree-phase induction motors play a crucial role in industrial operations. However, failures in these machines can lead to significant operational issues, affecting both productivity and safety. Traditionally, fault detection in induction motors has been carried out using conventional techniques, such as time and frequency domain analysis, utilizing characteristic signatures from vibration and current. Although these approaches can be effective in some cases, they face limitations in terms of accuracy and their ability to handle large volumes of complex data, particularly under variable operational conditions. In light of these challenges, this study proposes the application of an Artificial Intelligence (AI) technique for fault diagnosis and classification, aiming to identify issues at early stages more efficiently and robustly, overcoming the limitations of traditional methods. The analysis was conducted using current, temperature, and vibration signals. Experiments were carried out on a test bench, simulating real operational conditions, including stator phase unbalance, bearing damage, and shaft misalignment. For fault classification, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed, a predictive architecture tailored for classification tasks. The optimal network configuration was determined through a selection process using a scanning method with multiple training iterations, followed by the introduction of new data to validate its efficiency. Tests conducted with the additional data demonstrated the high performance of the neural network, showcasing its ability to generalize across all evaluated conditions. The accuracy rates ranged from 94.2% to 98%, depending on the machine’s operational state.Doctoral Thesis Inteligência artificial aplicada ao ecossistema de regulação do Estado Rio Grande do Norte (RegulaRN): análises baseadas em machine learning em leitos Covid-19 e leitos gerais(Universidade Federal do Rio Grande do Norte, 2024-09-27) Barreto, Tiago de Oliveira; Valentim, Ricardo Alexsandro de Medeiros; https://orcid.org/0000-0002-9216-8593; http://lattes.cnpq.br/3181772060208133; https://orcid.org/0000-0002-4399-9518; http://lattes.cnpq.br/7875886647905077; Dória Neto, Adrião Duarte; Cortez, Lyane Ramalho; Morais, Antonio Higor Freire de; Machado, Guilherme Medeiros; Santos, João Paulo Queiroz dosThe process of bed regulations is among the most relevant processes for the Brazilian public health system. It encompasses the entire process of managing and monitoring a patient who requires hospitalization, from the request to their proper admission. However, it is still an area that has little investment in digital health systems and other resources that can favor the better management of the regulatory process. Thus, this work aims to include the area of artificial intelligence within the area of regulating public beds, in order to enhance and assist the decision-making process during bed regulation. In this sense, bed regulation data from two modules of the platform adopted in Rio Grande do Norte, RegulaRN COVID-19 and RegulaRN Leitos Gerais, were used in order to classify the data and predict the patient's outcome. In total, approximately 72,422 bed regulation data were analyzed in different time frames. In addition, a pipeline of characterization, preprocessing, data correlation, definition of metrics for evaluation, data balancing, definition of training and validation data, definition of computational models for data classification and selection of hyperparameters was used. For the RegulaRN COVID-19 platform, the results showed better performance for the accuracy (84.01%), precision (79.57%) and F1-score (81.00%) metrics in the Multilayer Perceptron (MLP) model with Stochastic Gradient Descent (SGD) optimizer. For the recall (84.67%), specificity (84.67%) and ROC-AUC (91.6%) metrics, the best results were obtained by Root Mean Squared Propagation (RMSProp). As for the RegulaRN Leitos Gerais data, the analyses were performed with two datasets: adults and pediatric and neonatal. For the first set, Extreme Gradient Boosting (XGBoost) presented the best accuracy (87.77%) and recall (87.77%) values, Random Forest had the best precision (87.05%), Gradient Boosting had the best F1 Score (87.56%) and for specificity (82.94%) it was obtained by SGD. For the newborn data, the best accuracy (87.50%), recall (87.50%) and F1-Score (88.48%) values were obtained by the Decision Tree classifier, the best precision (90.75%) by Adaboost and the best specificity by MLP Adam. The results allowed us to identify the best models to assist health professionals during the bed regulation process, as well as the scientific findings of this academic work demonstrate that the computational methods used applied through a digital health solution can assist in the decision-making of medical regulators and government institutions in order to strengthen the performance of Brazilian public health.