https://repositorio.ufu.br/handle/123456789/5142 2026-04-28T20:39:27Z https://repositorio.ufu.br/handle/123456789/48631 Title: Visualização e Interação no Tesouro Direto: Uma Análise de Usabilidade e Acessibilidade na Seção de Histórico de Preços e Taxas 2026-03-20T00:00:00Z https://repositorio.ufu.br/handle/123456789/48612 Title: Modelo de otimização da grade horária para discentes da FACOM/UFU 2026-03-25T00:00:00Z https://repositorio.ufu.br/handle/123456789/48556 Title: Aprendizado de redes neurais profundas para diagnóstico molecular rápido e não invasivo de COVID-19 pela saliva Abstract: The COVID-19 pandemic exposed limitations of conventional diagnostic methods, such as RT-PCR, which, despite their high sensitivity, are costly, time-consuming, and logistically complex. Vibrational spectroscopy emerges as a promising alternative, offering rapid, low-cost, and reagent-free analyses. However, the complexity of biochemical changes reflected in Raman and ATR-FTIR spectra demands advanced analytical methods that surpass traditional chemometric approaches in extracting nonlinear patterns. This work develops and evaluates models based on deep learning, specifically Convolutional Neural Network (CNN) architectures and Convolutional Neural Networks integrated with Long Short-Term Memory (CNN-BiLSTM), applied to the analysis of non-invasive biological sample spectra for COVID-19 detection. CNNs offer superior capability for extracting local spatial features from spectra through convolution operations, while BiLSTM networks complement this analysis by capturing bidirectional temporal dependencies in sequential spectral data, enabling a more comprehensive understanding of complex spectral patterns. Two architectures were developed: CNN-Spectra was designed to analyze Raman spectra from blood serum, achieving 96.8% accuracy; CNN-BiLSTM-Spectra was developed to analyze ATR-FTIR spectra from saliva, reaching an average accuracy of 80% and outperforming traditional and state-of-the-art models. The results confirm the effectiveness of the proposed models in extracting relevant features from complex spectral data, improving classification between positive and negative COVID-19 samples. It is concluded that the integration of deep learning with vibrational spectroscopy is a promising approach for biomedical diagnosis, contributing to faster, more accurate, and sustainable detection methods. 2025-09-22T00:00:00Z https://repositorio.ufu.br/handle/123456789/48491 Title: Redes complexas para classificação de alto nível de assinaturas vibracionais moleculares Abstract: Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy is a promising technique for rapid and non-invasive diagnostics, yet its clinical application is often constrained by significant analytical challenges. The high dimensionality, complexity, and spectral overlap inherent in real-world data make it difficult to distinguish between different pathological conditions, a task where both conventional classifiers and deep learning models have shown limitations. To address these challenges, we propose ATLA (ATR-FTIR Topological Learning Analysis), a novel framework that integrates topological, structural, and physical information for enhanced spectral classification. Our methodology leverages the feature extraction capabilities of Complex Networks in synergy with traditional classifiers (Support Vector Machines and Linear Discriminant Analysis) and a state-of-the-art deep learning architecture (Convolutional Neural Network-BiLSTM). We assessed ATLA's performance on challenging real-world ATR-FTIR datasets for the diagnosis of COVID-19, Hepatitis B and D, and Chagas Disease. Our findings indicate that ATLA significantly improves predictive outcomes across key metrics, including accuracy, precision, sensitivity, specificity, and F1-score. Beyond predictive power, ATLA also enhances model interpretability by using SHAP (SHapley Additive exPlanations) to highlight the most relevant spectral bands and topological features driving the classification. Ultimately, ATLA emerges as a robust and effective approach, poised to elevate spectral diagnostics to a new level of accuracy and interpretability. 2025-09-25T00:00:00Z