ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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- 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.
- 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.
- 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.
- Evolution of Demand for Portuguese Thermal Spas: An Exploratory Data Analysis of Administrative DataPublication . Alves, Jéssica; Alves, Maria José; Nunes, AlcinaWell-being or positive health is not a new concept; However, since the mid-twentieth century, scientific institutions have addressed the issue and its implications for medicine and public health. It can be observed that wellness and healthy living are becoming more and more important in society. Consequently, there is na increased need to look for products and services that mitigate the risk of these diseases associated with modern society. Thus, a fusion between tourism and wellness emerges, with thermal spas being an essential factor in the health and wellness promotion equation. In Portugal, thermal services are well known. However, the demand for its services is subject to difficulties well observed during the COVID-19 pandemic. Therefore, it is fundamental to characterize the Portuguese thermal market on the demand side to segment it and find the best strategy to meet the user needs. Secondly, it is relevant to understand the weight of each segment in the market. Focusing on the division between classic thermal services—considered therapeutical health services—and wellness services provided by Portuguese termal establishments, this research aims to characterize the thermal market on the demand side. In particular, the study seeks to understand how wellness services are evolving regarding the total demand for Portuguese thermal establishments. Based on the descriptive analysis of administrative data of registers in all the Portuguese termal establishments, it is possible to conclude that the increasing demand for thermalism products and services (both therapeutic and wellness) suffered a severe crash with the financial crisis and the Covid-19 pandemic. Age is an essential factor that distinguishes the market segment of the therapeutic thermal offer. The consumers of such a thermal component are older than the ones demanding wellness services. Finally, specific international markets (France, Spain, and the United Kingdom) become more important in the user’s segments.
- 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.
- 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.
- Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning TechniquesPublication . Mendes, João; Moso, Juliet; Berger, Guido; Lima, José; Costa, Lino; Guessoum, Zahia; Pereira, Ana I.Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.
- Schizophrenia diagnosis support with spectral and cepstral features of speechPublication . Teixeira, Felipe; Mendes, João; Soares,Salviano F. P.; Abreu, J. L. Pio; Teixeira, João PauloSchizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3%, a sensitivity of 94.6%, and an F1-score of 95.4%. These results highlight the potential of speech-based machine learning models as effective tools for aiding schizophrenia diagnosis.