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.

2023Journal article

Open access

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%.

2022Journal article

Open access

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.

2022Journal article

Open access