ESTGV - DEMGI - Artigo em revista científica, indexada ao WoS/Scopus
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- Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case StudyPublication . Abbasi, Maryam; Bernardo, Marco V.; Vaz, Paulo; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, hetero geneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.
- Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial AttacksPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroThe rise of deepfakes—synthetic media generated using artificial intelli gence—threatens digital content authenticity, facilitating misinformation and manipu lation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures—XCeption, ResNet, and VGG16—for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, XCeption achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These find ings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deep fake threats
- Data Privacy and Ethical Considerations in Database ManagementPublication . Pina, Eduardo; Ramos, José; Jorge, Henrique; ANTUNES VAZ, PAULO JOAQUIM; Vaz, Paulo; Silva, José; Wanzeller, Cristina; Abbasi, Maryam; Martins, Pedro; Silva, José; Wanzeller Guedes de Lacerda, Ana CristinaData privacy and ethical considerations ensure the security of databases by respecting individual rights while upholding ethical considerations when collecting, managing, and using information. Nowadays, despite having regulations that help to protect citizens and organizations, we have been presented with thousands of instances of data breaches, unauthorized access, and misuse of data related to such individuals and organizations. In this paper, we propose ethical considerations and best practices associated with critical data and the role of the database administrator who helps protect data. First, we suggest best practices for database administrators regarding data minimization, anonymization, pseudonymization and encryption, access controls, data retention guidelines, and stakeholder communication. Then, we present a case study that illustrates the application of these ethical implementations and best practices in a real-world scenario, showing the approach in action and the benefits of privacy. Finally, the study highlights the importance of a comprehensive approach to deal with data protection challenges and provides valuable insights for future research and developments in this field
- Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting ConditionsPublication . Abbasi, Maryam; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.
- Fatigue behaviour of glass fibre reinforced epoxy composites enhanced with nanoparticlesPublication . Borrego, L.P.; Costa, J.D.M.; Ferreira, J.A.M.; Silva, H.Nanoparticle reinforcement of the matrix in laminates has been recently explored to improve mechanical properties, particularly the interlaminar strength. This study analyses the fatigue behaviour of nanoclay and multiwalled carbon nanotubes enhanced glass/epoxy laminates. The matrix used was the epoxy resin Biresin CR120, combined with the hardener CH120-3. Multiwalled carbon nanotubes (MWCNTs) 98% and organo-montmorillonite Nanomer I30 E nanoclay were used. Composites plates were manufactured by moulding in vacuum. Fatigue tests were performed under constant amplitude, both under tension–tension and three points bending loadings. The fatigue results show that composites with small amounts of nanoparticles addition into the matrix have bending fatigue strength similar to the obtained for the neat glass fibre reinforced epoxy matrix composite. On the contrary, for higher percentages of nanoclays or carbon nanotubes addition the fatigue strength tend to decrease caused by poor nanoparticles dispersion and formation of agglomerates. Tensile fatigue strength is only marginally affected by the addition of small amount of particles. The fatigue ratio in tensio –tension loading increases with the addition of nanoclays and multi-walled carbon nanotubes, suggesting that both nanoparticles can act as barriers to fatigue crack propagation.
- Head-to-Head Evaluation of FDM and SLA in Additive Manufacturing: Performance, Cost, and Environmental PerspectivesPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Martins, Pedro; Silva, JoséThis paper conducts a comprehensive experimental comparison of two widely used additive manufacturing (AM) processes, Fused Deposition Modeling (FDM) and Stereolithography (SLA), under standardized conditions using the same test geometries and protocols. FDM parts were printed with both Polylactic Acid (PLA) and Acryloni trile Butadiene Styrene (ABS) filaments, while SLA used a general-purpose photopolymer resin. Quantitative evaluations included surface roughness, dimensional accuracy, ten sile properties, production cost, and energy consumption. Additionally, environmental considerations and process reliability were assessed by examining waste streams, recy clability, and failure rates. The results indicate that SLA achieves superior surface quality (Ra ≈ 2 µm vs. 12–13 µm) and dimensional tolerances (±0.05 mm vs. ±0.15–0.20 mm), along with higher tensile strength (up to 70 MPa). However, FDM provides notable ad vantages in cost (approximately 60% lower on a per-part basis), production speed, and energy efficiency. Moreover, from an environmental perspective, FDM is more favorable when using biodegradable PLA or recyclable ABS, whereas SLA resin waste is hazardous. Overall, the study highlights that no single process is universally superior. FDM offers a rapid, cost-effective solution for prototyping, while SLA excels in precision and surface finish. By presenting a detailed, data-driven comparison, this work guides engineers, product designers, and researchers in choosing the most suitable AM technology for their specific needs.
- Interlaminar Adhesive Strength of Nano-Reinforced Glass/Epoxy LaminatesPublication . Silva, H.; Ferreira, J. A. M.; Costa, J. D. M.; Capela, C.Interlaminar fracture is significantly influenced by the adhesive strength of fiber=matrix interfaces. Critical strain energy release rate (GC) is the most common parameter used to quantify the interfacial strength. However, subcritical debonding can occur at lower mechanical loads than those required for interlaminar fracture toughness (IFT). This study was performed using nanoclayreinforced epoxy=glass fiber laminates in order to analyze the influence of the addition of nanoclay and hydro aging on IFT and subcritical crack growth. Hydro aging was done immersinga batch of specimens in distilled water at 25 C for 30 days. Mode I IFT was significantly improved by the incorporation of nanoclays into the resin, the improvement reaching 31% for 3% of nanoclays content. The results of subcritical debonding were plotted in terms of da=dt versus G curves, for dry materials and long term hydro aged composites. Hydro aged composites exhibit not only a reduction of GIc, of about 14% for 3% of nanoclays, but also a higher subcritical crack propagation rate. The addition of nanoparticles reduces subcritical crack propagation rate.
- Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data IntegrationPublication . Abbasi, Maryam; Vaz, Paulo; Silva, José; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIMThe efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environ mental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance.
- Mixed Mode interlayer fracture of glass fiber/nano-enhanced epoxyPublication . Silva, H.; Ferreira, J. A. M.; Capela, C.; Richardson, M.O.WIncreasing interlaminar fracture toughness (IFT) has long been an important goal in the fiber reinforced composites field. For that purpose some research has recently explored the use of nanoparticle reinforced matrices to improve interlaminar strength. In this present paper a small quantity of nanoclays (NC) and multiwalled carbon nanotubes (MWCNT) were used in order to enhance the IFT of glass fiber/epoxy composite laminates. The composites sheets were produced by a vacuum molding process. Mode I, Mode II, and Mixed-Mode I/II tests were performed to determine critical strain Energy release rates, using double cantilever beam, end-notched flexure, and Mixed-Mode Bending specimens, respectively. Significant improvements in IFT were obtained for all loading modes by the incorporation of NC into the epoxy resin, whilst MWCNT produced only moderate improvements. For Mode I, IFT improvement by the incorporation of nanoparticle fillers, reached about 31% for 3 wt% of NC and 17% for 1 wt% of MWCNT. In Mode II the increase was about 50% for 3 wt% of NC and 30% for 1 wt% of MWCNT. The dispersion of small quantities NC and MWCNT into matrices significantly improved Mixed-Mode IFTs for all loading mode ratios GII/G. The total fracture toughness G increased under Mode II loading components and linear Mixed-Mode fracture criteria reproduced the Gc versus Mode ratios GII/G and GI versus GII relationship.
- Performance and Scalability of Data Cleaning and Preprocessing Tools: A Benchmark on Large Real-World DatasetsPublication . Martins, Pedro; Cardoso, Filipe; Vaz, Paulo; Silva, José; Abbasi, MaryamData cleaning remains one of the most time-consuming and critical steps in modern data science, directly influencing the reliability and accuracy of downstream analytics. In this paper, we present a comprehensive evaluation of five widely used data cleaning tools—OpenRefine, Dedupe, Great Expectations, TidyData (PyJanitor), and a baseline Pandas pipeline—applied to large-scale, messy datasets spanning three domains (healthcare, finance, and industrial telemetry). We benchmark each tool on dataset sizes ranging from 1 million to 100 million records, measuring execution time, memory usage, error detection accuracy, and scalability under increasing data volumes. Additionally, we assess qualitative aspects such as usability and ease of integration, reflecting realworld adoption concerns. We incorporate recent findings on parallelized data cleaning and highlight how domain-specific anomalies (e.g., negative amounts in finance, sensor corruption in industrial telemetry) can significantly impact tool choice. Our findings reveal that no single solution excels across all metrics; while Dedupe provides robust duplicate detection and Great Expectations offers in-depth rule-based validation, tools like TidyData and baseline Pandas pipelines demonstrate strong scalability and flexibility under chunkbased ingestion. The choice of tool ultimately depends on domain-specific requirements (e.g., approximate matching in finance and strict auditing in healthcare) and the magnitude of available computational resources. By highlighting each framework’s strengths and limitations, this study offers data practitioners clear, evidence-driven guidance for selecting and combining tools to tackle large-scale data cleaning challenges