Escola Superior de Tecnologia e Gestão
Permanent URI for this community
Browse
Browsing Escola Superior de Tecnologia e Gestão by Sustainable Development Goals (SDG) "03:Saúde de Qualidade"
Now showing 1 - 10 of 34
Results Per Page
Sort Options
- 5th Symposium of Applied Science for Young Researchers: Book of AbstractsPublication . Fernandes, Florbela P.; Torres, Helena; Pinto, Pedro; Malta, SilvestreThese are the abstracts of the 5th Symposium of Applied Science for Young Researchers – SASYR. This scientific event welcomed scientific works on the topics covered by the following four research centers: – CeDRI (from IPB, Instituto Politécnico de Bragança) – 2Ai (from IPCA, Instituto Politécnico do Cávado e do Ave) – GECAD (from IPP, Instituto Politécnico do Porto) – ADiT-lab (from IPVC, Instituto Politécnico de Viana do Castelo) The primary objective of SASYR 2025 is to create a welcoming and relaxed environment for young researchers to present their work, discuss recent findings, and explore new ideas. In this way, this event offers an opportunity for the CeDRI, 2Ai, GECAD, and ADiT-lab research communities to leverage synergies and promote collaborations, thereby enhancing the quality of their research. The SASYR 2025 took place at Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal, on 2 July 2025.
- Aplicação de identificação de episódios de fibrilação atrialPublication . Guerreiro, Nathan Antonio; Teixeira, João Paulo; Dajer, Maria EugeniaAs Doenças Cardiovasculares (DCVs) causam cerca de 18 milhões de mortes por ano, segundo a Organização Mundial da Saúde (OMS). Na Europa, mais de 10 milhões de pessoas são afetadas anualmente, com 3 milhões de óbitos registrados em 2021. No Brasil, são aproximadamente 400 mil mortes por ano, resaltando arritmias cardíacas. A fibrilação atrial (FA), arritmia mais comum, é caracterizada por ritmo cardíaco irregular. Diante desse cenário e alinhado ao papel social do engenheiro e ao Objetivo de Desenvolvimento Sustentável (ODS) “Garantir saúde e bem-estar para todos” da Organização das Nações Unidas (ONU), este trabalho apresenta o desenvolvimento de uma interface gráfica do usuário (GUI) para aquisição e classificação de eletrocardiogramas (ECG). O sistema foi implementado em MATLAB, integrando aquisição em tempo real com a plataforma BITalino c (derivação I, via Bluetooth), detecção dos picos R e classificação automática de episódios de FA com redes neurais LSTM. São extraídas quatro características principais a cada 60 ciclos cardíacos: intervalos RR e entropias de Shannon das ondas T, U e P. Após normalização, essas variáveis compõem os vetores de entrada da rede, que classifica como Outro Ritmo, Ritmo Normal e Ritmo FA. A aplicação permite ainda a visualização dos sinais em tempo real e a geração automática de relatórios em PDF. A validação com sinais da base de dados MIT-BIH Atrial Fibrillation demonstrou que a interface é funcional, e a acurácia de 98,17%, obtida em estudo anterior, evidencia seu potencial como ferramenta auxiliar na análise de ECGs em ambientes clínicos e domiciliares.
- The artificial intelligence act: insights regarding its application and implicationsPublication . Cabrera, Beatriz M.; Luiz, Luiz E.; Teixeira, João PauloThis paper deals with Europe's Artificial Intelligence Act, the first regulation on the subject and is considered an international milestone. Therefore, the introduction provides a historical overview of legislative developments on Artificial Intelligence in Europe until the current milestone, namely the approval of the AI Act text, which is currently being amended and translated into its official publication. Afterwards, the regulation is dealt with in detail; its nuances are presented, along with the conceptualisation of artificial intelligence in the regulation and the classification of artificial intelligence systems, which is based on risks to users, together with the mechanisms created to make the regulation more efficient, specifically the Artificial Intelligence Office. Ultimately, considering the great innovation on the subject, this work presents different opinions regarding the application of the regulation, its risk-based analysis and classification, expectations and views on the possible impacts of the act on the market, thereby seeking to expose society's receptiveness to the regulation created. Therefore, based on the discussion points, it can be concluded that the regulation, which will soon be in effect, brings different feelings to citizens and members of the European market, who are still insecure about the risk-based approach used, harbouring feelings of fear about the limitation of innovation. However, at the same time, there is hope, given that regulation is necessary to guarantee safe innovation in line with the fundamental rights set out by the European Union. It can also be concluded that the approval and forthcoming publication of the act is a small step towards the challenges that will arise. It is certain that, regardless of the different opinions that exist, it is necessary to start implementing the act to analyse its effects on the market, society, politics, the economy and, above all, on innovations in artificial intelligence systems.
- Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approachPublication . Chaabani, Mohamed Khalil; Teixeira, João Paulo; Slim , Mohamed AymenCardiovascular diseases, including myocardial infarction, remain among the leading causes of mortality worldwide. Timely and accurate diagnosis is critical for effective treatment but often requires labour-intensive manual analysis of clinical-grade electrocardiograms (ECGs). This dissertation proposes a novel deep learning-based approach for binary classification of cardiac pathologies, using the PTB-XL dataset. The final model architecture integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies between ECG leads, the results prove its adaptability for ECG image classification tasks. Extensive experimentation and iterative model development were conducted to reach the final design. Early trials involved exploring different hyperparameter tuning like Optuna and Adam optimizer and a wide range of hyperparameter configurations, including different learning rates, dropout rates, batch sizes, and numbers of Linformer layers. These experiments were critical in finding the optimal combination of parameters that balanced computational efficiency and model accuracy. Comprehensive details of these trials and evaluations are provided in the report. The preprocessing pipeline involves selecting the ECGs and converting them to 11 images (aVR was excluded) each representing a lead, converting RGBA ECG images to RGB format and applying normalization to ensure compatibility with model input requirements. This preprocessing step addresses the unique format of the dataset and prepares it for high-performance neural network training. Initial results from the finalized model architecture have demonstrated promising performance, achieving an AUC (Area Under the Curve) of 85.02% and a F1-score of 78.94%. The achieved results are comparable to recent state-of-the-art models reported on the PTB-XL dataset, which typically range between 85% and 95% AUC for similar binary classification tasks. These results indicate that the model's AUC of 85.02% is promising but on the edge of the current state-of-the-art. These findings indicate strong potential for the system to support clinical decision-making by automating the classification of ECG data. Ongoing research aims to extend the current binary classification framework to multi-class scenarios, further enhancing its clinical applicability. Additionally, efforts are being made to improve the model for faster inference times, enabling real-time ECG analysis and improving its feasibility for deployment in healthcare settings.
- Classical Versus Wellness Thermalism: The Case of Portuguese Thermal Establishments Before and After the COVID-19 PandemicPublication . Alcina Nunes; Jéssica Alves; Estelle Gonçalves; Ana Margarida Pereira; Maria José Alves; Nunes, Alcina; Alves, Maria JoséThermal/mineral springs are one of the fastest-growing subcategories of wellness tourism. Indeed, it is an activity that has steadily increased in all of Europe’s developed economies over the last few decades. The pandemic has raised awareness of the importance of healthy lifestyles and has subsequently led to a surge in consumption of experiences and travel, somehow motivated by wellness. This study analyses the evolution of thermal users’ alternation between wellness and classical thermalism in Portugal. The objective is achieved by applying exploratory and cluster data analysis to a Portuguese administrative database containing the number of user registers and revenues generated from 2012 to 2022. During this period, the wellness registers increased in most thermal establishments compared to the classic records, even if service diversification may be found in most thermal establishments. Still, the financial value added by wellness consumers does not seem to follow the previously observed shift. The establishments with more classical registers are still the ones that are able to generate the highest income per person.
- Colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep Learning Model PerformancePublication . Araujo, Sandro Luis de; Scheeren, Michel Hanzen; Aguiar, Rubens Miguel Gomes; Mendes, Eduardo; Franco, Ricardo Augusto Pereira; Paula Filho, Pedro Luiz deColorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing these polyps through colonoscopy is a crucial preventative measure. However, even experienced professionals can overlook some polyps during examinations. In this context, segmentation algorithms can assist medical professionals by identifying areas in an image that correspond to a polyp. These algorithms, which rely on deep learning, require extensive image datasets to effectively learn how to identify and segment polyps. This study aimed to identify public colonoscopy image datasets that contain polyps and to examine how combining these datasets might affect the performance of a deep learning-based segmentation algorithm. After selecting the datasets and defining their combinations, we trained a segmentation algorithm on each combination. The evaluation of the trained models showed that merging datasets can enhance model generalization, with increases of up to 0.242 in the dice coefficient and 0.256 in the Intersection over Union (IoU). These improvements could lead to higher diagnostic accuracy in clinical settings, enhancing efforts to prevent colorectal cancer.
- Comparison of neural network architectures for diabetes predictionPublication . Guerreiro, Nathan Antonio; Nijo, Rui; Teixeira, João PauloDiabetes represents a significant global health challenge, with millions of individuals affected and substantial impacts on healthcare systems. In this study, we compare two neural network architectures for diabetes prediction: the Feedforward Neural Network (FFNN) and the Cascade-Forward Backpropagation Neural Network (CFBPNN). Utilizing the Diabetes Prediction Dataset, comprising 100,000 samples, and after a balanced result, 17,000 samples were obtained. The networks are trained using the Levenberg-Marquardt and Resilient Backpropagation algorithms, and performance metrics, including precision, sensitivity, specificity, accuracy, F1-score, and computational time, are evaluated. Results indicate that the FFNN architecture paired with the Levenberg-Marquardt algorithm demonstrates superior diagnostic prediction accuracy with 91,10%. However, this comes at the cost of longer computational time compared to the CFBPNN.
- Contributions to accelerating a numerical simulation of free flow parallel to a porous planePublication . Schepke, Claudio; Spigolon, Roberta A.; Rufino, José; Cristaldo, Cesar F. Da C.; Pizzolato, Glener L.Flow models over flat p orous surfaces have applications in natural processes, such as material, food, chemical processing, or mountain mudflow simulations. The development of simplified a nalytical or numerical models can predict characteristics such as velocity, pressure, deviation length, and even temperature of such flows for geophysical and engineering purposes. In this context, there is considerable interest in theoretical and experimental models. Mathematical models to represent such phenomena for fluid mechanics have continuously been developed and implemented. Given this, we propose a mathematical and simulation model to describe a free-flowing flow pa rallel toa porous material and its transition zone. The objective of the application is to analyze the influence o f t he p orous matrix on the flow u nder d ifferent m atrix p roperties. W e i mplement a Computational Fluid Dynamics scheme using the Finite Volume Method to simulate and calculate the numerical solutions for case studies. However, computational applications of this type demand high performance, requiring parallel execution techniques. Due to this, it is necessary to modify the sequential version of the code. So, we propose a methodology describing the steps required to adapt and improve the code. This approach decreases 5.3% the execution time of the sequential version of the code. Next, we adopt OpenMP for parallel versions and instantiate parallel code flows and executions on multi-core. We get a speedup of 10.4 by using 12 threads. The paper provides simulations that offer the correct understanding, modeling, and construction of abrupt transitions between free flow a nd porous media. The process presented here could expand to the simulations of other porous media problems. Furthermore, customized simulations require little processing time, thanks to parallel processing.
- D.R.E.A.M. App to Promote the Mental Health in Higher Education StudentsPublication . Vaz, Clara B.; Pais, Clarisse; Pinheiro, MarcoThis paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher educa- tion students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google’s Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, struc- tured physical activity programs, development of socio-entrepreneurial skills, and vocational area.
- Development of cosmetic functional formulations incorporating hyaluronic acidPublication . Silva, Thaís Rossetto Cordeiro da; Barreiro, Filomena; Echart, Arantzazu Santamaria Echart; Düsman, ElisângelaThe cosmetic industry has been expanding continuously, driven by demand for innovative, sustainable, and high-performance formulations. In this context, this work aimed to develop particles composed of hyaluronic acid (HA) and Collagen type I (Col) to act as Pickering stabilisers in cosmetic emulsions. The particles were prepared using different methods (direct mixing - mix and droping - drop), with the most promising dispersions obtained after high-shear homogenisation: P_17_Mix_Ultra and P_17_Drop_Ultra. These formulations exhibited particle sizes of 12.30 ± 3.73 μm and 2.55 ± 0.08 μm, respectively. Both showed highly negative zeta potentials (–37.73 ± 1.50 mV for the mix and –37.72 ± 0.56 mV for the drop), confirming colloidal stability. Wettability tests demonstrated strong oil affinity, with contact angles of 128.30 ± 1.05° and 137.22 ± 3.20°. The drop method stood out for producing smaller, more homogeneous particles. Pickering emulsions were then prepared using sweet almond oil, Miglyol 812, and olive oil. Olive oil originated the most stable systems, and emulsions with a higher oil fraction (70:30 oil-to-aqueous ratio) presented superior stability compared to those with lower oil content. Analyses of emulsion type, colourimetry, creaming index, droplet size, microscopy, zeta potential, and rheology confirmed the better performance of olive oil-based formulations obtained by the drop method. The formulation E_70_OO_17d (drop method, olive oil, 70:30 ratio) achieved the most promising overall results, with a creaming index of 10% after 30 days, homogeneous droplets, and rheological behaviour exhibiting viscoelastic properties comparable to those of commercial products. In conclusion, HA-Col particles proved to be efficient Pickering stabilisers, enabling the development of stable emulsions. The most promising formulation, E_70_OO_17d, was particularly suitable for moisturising and anti-ageing cosmetic applications, where hyaluronic acid provides hydration and regeneration while Col enhances elasticity and repair.