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- 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.
- Classical Versus Wellness Thermalism: The Case of Portuguese Thermal Establishments Before and After the COVID-19 PandemicPublication . Alves, Maria José; Nunes, Alcina; Alves, Jéssica; Gonçalves, Estelle SilvaThermal/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.
- 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.
- D.R.E.A.M. App to Promote the Mental Health in Higher Education StudentsPublication . Vaz, Clara B.; Galvão, Ana Maria; 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 education 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, structured physical activity programs, development of socio-ntrepreneurial skills, and vocational area.
- Desenvolvimento de protótipo de sistema de suporte ao diagnóstico de patologias da vozPublication . Rodrigues, Patrícia Manuela Andrade; Teixeira, João Paulo; Rodrigues, Pedro JoãoRESUMOA voz é uma ferramenta de comunicação primordial nas relações inter-humanas, por meio de inflexões, pausas, variações de ritmo e de intensidade. É considerada a integridade da nossa identidade, pois através dela somos reconhecidos e a sua qualidade permite-nos expressar eficazmente. As patologias vocais encontram-se presentes na nossa sociedade, com profundo impacto na qualidade de vida das pessoas. A origem deve-se a várias causas e apresentam diferentes graus de gravidade. A patologia pode progredir de forma benigna ou maligna, por isso é de extrema importância ter atenção aos sinais de alteração. Um diagnóstico precoce é muito relevante para o tratamento. Porém, as formas de avaliação existentes nesta área são invasivas e desagradáveis, sendo incomodativas para o paciente. Estes aspetos motivaram o desenvolvimento de métodos não invasivos, que possam fazer uma avaliação exata e possam ser utilizados como um método de ajuda ao diagnóstico eficaz. Neste trabalho desenvolveu-se um sistema de suporte à decisão médica no diagnóstico de patologias vocais. Para o desenvolvimento deste sistema foi necessário o estudo de um conjunto de parâmetros acústicos, bem como de classificadores, como rede neuronal artificial (RNA), com o objetivo de fazer a classificação final do paciente entre saudável e patológico. Os parâmetros utilizados neste trabalho são: Jitter Absoluto (Jitta), Jitter Relativo (Jitter), Shimmer Absoluto (ShdB), Shimmer Relativo (Shim) , Harmonic to Noise Ratio (HNR), e a Autocorrelacão. E como classificador o modelo da rede neuronal Multi Layer Perceptron (MLP). O sistema interface gráfica desenvolvido neste trabalho servirá como um método complementar no pré-diagnóstico de patologias da voz. O modelo MLP utilizada obteve uma taxa de exatidão de 98.86% que se encontra entre os melhores valores tendo em conta estado a arte, no entanto a possibilidade da inserção deste sistema em clínicas e hospitais contribuirá para o seu aperfeiçoamento por meio de familiarização com profissionais de saúde.
- 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.
- A machine learning approach for enhanced glucose prediction in biosensorsPublication . Abreu, António; Oliveira, Daniela dos Santos; Vinagre, Inês; Cavouras, Dionisios; Alves, Joaquim A.; Pereira, Ana I.; Moreira, Felismina T. C.The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor’s performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75–40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection.
- An openmodelica package for BELBICPublication . Coelho, João Paulo; Coelho, J. A. B.; Braz-César, ManuelThis paper presents the development and implementation of a new package for OpenModelica that integrates the Brain Emotional Learning Based Intelligent Controller (BELBIC) approach into control system simulations. BELBIC, inspired by neurobiological models of emotional learning, has demonstrated effectiveness in handling complex, nonlinear, and adaptive control problems. The proposed package provides a modular and user-friendly framework for integrating BELBIC enabling researchers and engineers to design, simulate, and analyze intelligent control strategies within an open-source environment. Key features of the package include customizable emotional response parameters and compatibility with existing Modelica libraries. To validate the package, a set of examples are included which demonstrates its application to the control of common dynamic systems.