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Percorrer Instituto Superior de Engenharia por Objetivos de Desenvolvimento Sustentável (ODS) "03:Saúde de Qualidade"
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- Air pollution forecasting using autoencoders: a classification-based prediction of NO2, PM10, and SO2 concentrationsPublication . Rodríguez-García, María Inmaculada; Carrasco-García, María Gema; Fernández, Paloma Rocío Cubillas; Ribeiro, Conceição; Cardoso, Pedro; Turias, Ignacio. J.This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values.
- Minute city concept for healthy tourism during the COVID-19 pandemic crisis. who for? The city of Lagos, PortugalPublication . Pires Rosa, Manuela; Lopes, Ana; Aghaeizadeh, Esmaeil; Gomes, André; Andraz, JorgeUrban spatial organization provided an important contribution for healthy tourism in the first COVID-19 pandemic period. The 15-Minute City concept promotes walking which is essential for the development of outdoor activities for a healthier tourism. The tourist city of Lagos (Portugal) is a pilot city of the Interreg Med SuSTowns Project which aims to promote sustainable and resilient territories. This study presents an analysis of the pedestrian accessibility through a specific geographical indicator: the percentage of short-term rentals existing in the surrounding tourist attractions. Ideal standard distances, studied internationally, were considered to assess this close proximity. Different walking speeds were considered to address human diversity and their implications on the 15-Minute City concept. The functionalities of the geographic information systems, in particular the assessment of distances over the pedestrian network were used. The results indicate that in the city of Lagos there is a trend towards an effective 15-Minute City for healthy tourists, promoting walking which could enhance tourism attraction. For tourists with reduced mobility, the use of accessible public transportation is required and needs to be integrated in the 15-Minute City concept.
- PneumoNet: artificial intelligence assistance for pneumonia detection on X-raysPublication . Antunes, Carlos; Rodrigues, Joao; Cunha, AntónioPneumonia is a respiratory condition caused by various microorganisms, including bacteria, viruses, fungi, and parasites. It manifests with symptoms such as coughing, chest pain, fever, breathing difficulties, and fatigue. Early and accurate detection is crucial for effective treatment, yet traditional diagnostic methods often fall short in reliability and speed. Chest X-rays have become widely used for detecting pneumonia; however, current approaches still struggle with achieving high accuracy and interpretability, leaving room for improvement. PneumoNet, an artificial intelligence assistant for X-ray pneumonia detection, is proposed in this work. The framework comprises (a) a new deep learning-based classification model for the detection of pneumonia, which expands on the AlexNet backbone for feature extraction in X-ray images and a new head in its final layers that is tailored for (X-ray) pneumonia classification. (b) GPT-Neo, a large language model, which is used to integrate the results and produce medical reports. The classification model is trained and evaluated on three publicly available datasets to ensure robustness and generalisability. Using multiple datasets mitigates biases from single-source data, addresses variations in patient demographics, and allows for meaningful performance comparisons with prior research. PneumoNet classifier achieves accuracy rates between 96.70% and 98.70% in those datasets.