ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Browsing ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus by Sustainable Development Goals (SDG) "04:Educação de Qualidade"
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- A prototype to enhance academic attendance using BLE beaconsPublication . Simas, Sily; Oliveira, Pedro Filipe; Matos, PauloThis project addresses the prototyping and application of an attendance control solution in an academic context, using Beacons. The introduction highlights the importance of effectively managing student attendance to ensure active participation in educational activities and provide insights into student engagement. BLE Beacons technology is compared to other RF signaling technologies, highlighting its advantages in terms of range, power consumption, cost, frequency, and data rate. BLE Beacon is chosen as the preferred technology due to its combination of adequate range, low power consumption, affordable cost, compatibility, security, and flexibility. The results section presents Estimote Beacons as devices used, highlighting their advanced features and techniques used, such as proximity and triangulation, to determine student presence. A visual summary of the project is presented in a flowchart that illustrates the operating processes of each stage. This solution promises to significantly improve the efficiency, safety, and quality of the academic environment, while simplifying the attendance recording process and providing valuable insights into student behavior.
- Automated preprocessing of olive leaf images for cultivar classification using YOLO11Publication . Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I.Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.
- Categorizing Students of the MathE Platform: A Fuzzy Clustering PerspectivePublication . Leite, Gabriel A.; Azevedo, Beatriz Flamia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.Active learning and technology integration offer enhanced student engagement and adaptive learning, accommodating diverse preferences. This work uses fuzzy clustering method to analyze the data of students who answer questions on the MathE platform. To do this, the Fuzzy c-means algorithm was used, which allows flexibility and adaptability in the clustering partitioning, especially in situations where data elements may exhibit overlapping characteristics or belong to multiple categories. Thereby, two datasets are considered: the first is composed of 121 students who answered questions from the Vector Space subtopic, and the second dataset comprises the answers of 297 students who answered to any topic or subtopic of the platform. The results show that the fuzzy clustering method is appropriate for analyzing the student’s data since most students are highly associated with more than one cluster. Besides, the findings can support the formulation of intervention strategies to improve the student’s academic achievement.
- Comparing RL policies for robotic pusherPublication . Bonjour, Pedro; Lopes, Rui PedroReinforcement learning (RL) has been consolidated as a promising approach to optimizing robotic tasks, allowing the improvement of performance and energy efficiency. This study investigates the effectiveness of five RL algorithms in the Pusher environment. Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy Gradient (TD3). We evaluated training time, computational efficiency, and reward values to identify the most balanced solution between accuracy and energy consumption. The results indicate that the PPO offers the best compromise between performance and efficiency, with reduced training time and stability in learning. SAC achieves the best rewards but requires more training time, while A2C faces difficulties in continuous spaces. DDPG and TD3, despite t he good results, have high computational consumption, which limits their viability for real-time industrial applications. These findings highlight the importance of considering energy efficiency when choosing RL algorithms for robotic applications. As a future direction, we propose the implementation of these algorithms in a real-world environment, as well as the exploration of hybrid approaches that combine different strategies to improve accuracy and minimize energy consumption.
- Data Engineering Roadmap for Implementing Business Intelligence in Higher EducationPublication . Sequeira, Romeu; Reis, Arsénio; Branco, Frederico; Alves, PauloThis article addresses the implementation of Business Intelligence (BI) systems in Higher Education Institutions (HEIs), focusing on developing an appropriate data architecture that meets the specificities and requirements of this sector. With the rapid advance of information technologies, HEIs face the growing challenge of managing a considerable volume of data, making it essential to implement BI systems that support informed and efficient decision-making. Using the Design Science Research methodology, this study proposes a BI architecture model that aligns technologies with HEIs' academic and administrative needs and facilitates their integration and ongoing maintenance. The model is designed to be flexible and scalable, allowing adaptations as institutional needs evolve. The article describes the architecture development process, from initial planning to implementation, and discusses how this framework can significantly improve data management and the quality of decision-making processes in educational institutions. The research offers practical and theoretical insights for academics and managers seeking to optimize the use of BI in educational contexts.
- A deep learning approach for average height estimation in oak colony using rgb imagesPublication . Britto, Raphael Duarte; Mendes, João; Grilo, Vinicius; Castro, João Paulo; Santos, Murillo Ferreira dos; Castro, Marina; Pereira, Ana I.; Lima, JoséMany strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.
- Design and development of a differential drive platform for dragster competitionPublication . Grilo, Vinicius; Ferreira, Edilson; Barbosa, Ana; Chellal, Arezki Abderrahim; José L. LimaRobotics competitions have been increasing in the last years since they bring several impacts on students education, such as technical skill development, teamwork, resilience and decision making withing the STEM skills. The article highlights the significance of robotics competitions as platforms for fostering innovation and driving advancements in the field of robotics. This article primarily focuses on the development of a robot in the Dragster category for the 2023 Portuguese Robotics Open. It outlines the strategies devised to tackle the competition’s challenges and discusses the obstacles encountered along with the corresponding solutions employed. The article delves into the specific details of the challenges faced and the iterative processes undertaken to enhance the robot’s performance and functionalities. By sharing the insights gained from the project, future proposals for iterations of the robot will be presented, aiming to further augment its features and overall performance while sharing knowledge with other teams and community.
- Forecasting COVID-19 in european countries using long short-term memoryPublication . Carvalho, Kathleen; Teixeira, Rita de Almeida; Reis, Luis Paulo; Teixeira, João PauloEffective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.
- Influence of habits and comorbidities on liver diseasePublication . Leite, Gabriel A.; Azevedo, Beatriz Flamia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.The prevalence of hepatocellular carcinoma is expected to continue increasing worldwide, and its difficulty in early detection highlights the need for advanced monitoring technologies. As the disease progresses, it has a serious impact on patients’ health, and in severe cases, liver transplantation becomes the only viable solution, reinforcing its importance as a global health problem. This study proposes the use of different artificial intelligence methods to compare and understand them related to liver disease. Well-known algorithms such as Random Forest and Multi-Layer Perceptron were tested, as well as ensemble methods that exploit different modeling structures. The results showed that AdaBoost, Random Forest, and Gradient Boosting performed best with Area Under the Curve of 0.89, 0.86, and 0.84 respectively. To analyze their influence on clinical results, the best-performing model was reapplied only to the non-biochemical features that compose the dataset. The results indicate that portal vein thrombosis, diabetes, and hypertension are the most influential variables, with contributions of 29.48%, 20.50%, and 16.60%, respectively.
- Interconnection between lifestyle, health, and academic outcomes: an analysis on study habits and well-beingPublication . Azevedo, Beatriz Flamia; Bezerra, Ana J.B.; Sirmakessis, Spiros; Pereira, Ana I.Balancing academic demands with personal and professional responsibilities has become an increasingly challenging task, making it difficult to maintain well-being and potentially leading to serious health problems. The stress resulting from these multiple daily tasks, combined with the pressure to perform at high academic levels, directly impacts students’ mental and emotional health, significantly compromising their quality of life. In this work, statistical and clustering techniques are employed to analyze the dataset “Daily Lifestyle and Academic Performance of Students”. The objective of this work is to explore the relationship between students’ daily habits, level of stress, and the impact on academic performance. The results point out that many students have difficulty managing time and maintaining well-being (low-stress levels) with high academic performance since, according to the results, the higher the academic outcome, the higher the student’s stress level.