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Projeto de investigação
User Profiling: An AGM-Based Belief Revision Approach Applied to Dynamic of Profiles
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Public perceptions, knowledge, responsibilities, and behavior intentions on marine litter: Identifying profiles of small oceanic islands inhabitants
Publication . Bettencourt, Sara; Freitas, Diogo Nuno; Costa, Sónia; Caeiro, Sandra
Marine litter is a global threat, particularly on oceanic islands where the problem is exacerbated. Perceptions,
knowledge, awareness, and attitudes towards the theme are crucial in its mitigation and prevention. This study
assessed these points through a questionnaire to the inhabitants of a Portuguese archipelago. Data revealed that
people associate marine litter with plastic and its impacts and are well informed about its sources and pathways.
Yet, the degradation rates of marine items were frequently underestimated and the problem of marine litter was
attributed, among others, to littering, single-use products, and excessive packaging. Some individuals did not
consider themselves responsible for reducing marine litter, attributing responsibilities to third parties. The
youngest group, men, and students were the ones who reported less litter-reducing intentions and behaviors.
Distinct profiles were traced using the questionnaire’s answers, highlighting who needs marine litter literacy.
Individuals who do not consider marine litter a current threat and live in a community that does not care about
marine litter (profiles 1 and 2) were the groups that needed deeper intervention, due to their low perception and
understanding of the problem. Marine litter literacy, management, and governance measures are necessary so
that the public recognizes marine litter as a current threat, is worried about its impacts, avoids plastic use, and
choses re-useable products (profile 4). In the studied oceanic islands, results indicated marine litter is not fully
perceived by the public. A global and transformative shift in the way people are educated and behave towards
waste and pollution is required, thereby highlighting the importance of increasing public perceptions assessment
and marine litter literacy in the society.
Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection
Publication . Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Freitas, Diogo; Dias, Fernando Morgado; Ravelo-García, Antonio G.
Methodologies for automatic non-rapid eye movement and cyclic alternating pattern
analysis were proposed to examine the signal from one electroencephalogram monopolar derivation
for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments.
A population composed of subjects free of neurological disorders and subjects diagnosed with
sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye
movement and A phase estimations, examining a one-dimension convolutional neural network (fed
with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram
signal or with proposed features), and a feed-forward neural network (fed with proposed features),
along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter
tuning algorithms were developed to optimize the classifiers. The model with long short-term
memory fed with proposed features was found to be the best, with accuracy and area under the
receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification,
while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The
cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic
alternating pattern rate percentage error was 22%.
Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG
Publication . Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Freitas, Diogo; Dias, Fernando Morgado; Ravelo-García, Antonio G.
The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalo gram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible
clinical applications; however, there is a need to develop automatic methodologies to facilitate
real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels’
feature level fusion was proposed in this work and employed for the CAP A phase classification.
Two optimization algorithms optimized the channel selection, fusion, and classification procedures.
The developed methodologies were evaluated by fusing the information from multiple EEG channels
for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results
showed that both optimization algorithms selected a comparable structure with similar feature level
fusion, consisting of three electroencephalogram channels (Fp2–F4, C4–A1, F4–C4), which is in line
with the CAP protocol to ensure multiple channels’ arousals for CAP detection. Moreover, the
two optimized models reached an area under the receiver operating characteristic curve of 0.82, with
average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement
and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has
the advantage of providing a fully automatic analysis without requiring any manual procedure.
Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being
thus suitable for real-world application.
User profiling with feature selection and explainability: essays on three case studies across different domains
Publication . Freitas, Diogo Nuno Teixeira; Teixeira Freitas, Diogo Nuno; Dias, Fernando Manuel Rosmaninho Morgado Ferrão; Fermé, Eduardo Leopoldo
User profiling is the process of constructing a structured representation of the user
within a system. This representation includes information such as preferences, behaviors,
and characteristics. Based on the profile, the system can recommend services and products
or, in this work, suggest actions. Machine learning methods are commonly used to this
end, as they can identify complex patterns among large numbers of attributes.
However, not all attributes are relevant. High-dimensional datasets often contain
irrelevant, redundant, or noisy features that obscure valuable patterns and reduce model
accuracy. To address this, dimensionality reduction techniques—particularly feature
selection—are essential. Equally important is the ability to explain a model’s output, since
understanding why a model produces a given outcome builds trust and clarifies which
steps can change an undesirable situation.
This thesis applies feature selection, explainability, causal discovery, and machine
teaching techniques to user profiling. The goal is to support decision-making by identi fying the most relevant features, clarifying causal mechanisms, and ensuring that stake holders understand why recommendations are made. Specifically, we investigate the
mRMR (minimum-Redundancy-Maximum-Relevance) method for feature selection, ex amine explainability strategies such as feature importance analysis and counterfactuals,
apply causal discovery to map cause-and-effect relationships, and use machine teaching
to explore profile simplification.
We apply this approach in four domains: (i) Marine litter: developing static profiles to
identify those who could benefit from literacy interventions; (ii) Football injuries: building
predictive models based on player profile dynamics to forecast risk; (iii) Energy poverty:
designing models, using counterfactuals, and applying causal discovery to understand
health–poverty links; and (iv) Concept complexity: using machine teaching to study
profile simplification.
These applications show how profiling can deliver targeted literacy interventions,
prevent sports injuries, inform preventive policies in energy poverty, and improve the
efficiency of user representations and concept learnability.
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Fundação para a Ciência e a Tecnologia
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Número da atribuição
2021.07966.BD