https://repositorio.ufu.br/handle/123456789/18920 Fri, 24 Jul 2026 23:03:08 GMT 2026-07-24T23:03:08Z https://repositorio.ufu.br/handle/123456789/48953 Title: Classificação de alto nível baseada em meta-grafos de grafos de visibilidade para detecção do Autismo Abstract: This study explores advanced classification techniques for the detection of Autism Spectrum Disorder (ASD) using ATR-FTIR spectroscopy applied to saliva samples. The proposed platform is fast, sustainable, and enables non-invasive sample collection. The resulting ATR-FTIR spectrum is a high-dimensional sequence that encodes molecular interactions, yet its sequential nature is often overlooked in related studies. While traditional ATR-FTIR approaches rely on low-level classifiers based on physical attributes, more recent high-level methods incorporate topological and structural features. However, these high-level methods face challenges in the graph construction phase, particularly in accurately capturing the sequential relationships within the data. To address this limitation, we propose VisG2, a meta-graph construction method that represents spectra as visibility graphs and computes their similarities to build a high-level structure. Experiments with real ASD data demonstrate that VisG2 outperforms both traditional and modern classifiers such as SVM, CNN and Transformers, as well as other commonly used graph construction techniques. Furthermore, when combined with low-level methods, VisG2 forms a hybrid classifier that further enhances the performance of the original high-level approach. The results confirm VisG2’s effectiveness in capturing sequential relationships and highlight its potential for ASD detection and other applications involving sequential data. Thu, 12 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48953 2026-02-12T00:00:00Z https://repositorio.ufu.br/handle/123456789/48829 Title: Uma arquitetura baseada em Sidecar para integração de criptografia pós-quântica no Core 5G Abstract: The rapid advancement of quantum computing poses a significant threat to the security foundations of 5G networks, which currently rely on classical ECC for signaling protection. This dissertation investigates the viability of integrating PQC into the 5G core to establish quantum-resistant communication, potentially via the SBI. Utilizing the free5gc-compose framework, a non-intrusive sidecar proxy architecture was developed and orchestrated via Docker, enabling the “PQC-retrofitting” of standard NFs without altering their underlying source code. The experimental setup evaluates three distinct architectural deployments: a Native (HTTPS/TLS) baseline, a TLS Proxy using traditional certificates, and a PQC Proxy utilizing NIST-standardized algorithms through specialized microservices (wrapper-kem and wrapper-sign). Latency was measured at the HTTP/2 request-response boundary on the SBI, with metrics captured across thirty independent repetitions to ensure statistical significance. The analysis incorporates both end-to-end SBI communication latency and proxy-side Container Metrics captured via an observability stack comprising Prometheus and cAdvisor. Preliminary results establish a baseline median latency of 15.07 ms for the Native architecture, providing a benchmark to quantify the performance overhead introduced by larger PQC key sizes and computational requirements. This work demonstrates that a sidecar approach provides a flexible, modular pathway for scuring 5G infrastructure against future quantum adversaries while maintaining the operational integrity of the core network. Wed, 25 Feb 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48829 2026-02-25T00:00:00Z 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. Mon, 22 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48556 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. Thu, 25 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48491 2025-09-25T00:00:00Z