Publications in peer-reviewed journals

P.R. Costa, C. Churro, S.M. Rodrigues, B. Frazão, M. Barbosa, L. Godinho, L. Soliño, V. Timóteo, N. Gouveia (2023). A 15-Year Retrospective Review of Ciguatera in the Madeira Islands (North-East Atlantic, Portugal). Toxins 15(630): DOI:10.3990/toxins15110630

A.C. Braga, S.M. Rodrigues, H.M. Lourenço, P.R. Costa, S. Pedro (2022). Bivalve shellfish safety in Portugal: variability of feacal levels, metal contaminants and marine biotoxins during the last decade (2011-2020). Toxins 15(2): 91, DOI:10.3390/toxins.15020091

R.C. Cruz, P.R. Costa, L. Krippahl, M.B. Lopes (2022). Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast wit Artificial Neural Networks. Knowledge-Based Systems 257: 109895, DOI:10.1016/j.knosys.2022.109895

A. Patrício, M.B. Lopes, P.R. Costa, R.S. Costa, R. Henriques, S. Vinga (2022). Time-lagged correlation analysis of shellfish toxicity reveals predictive links to adjacent areas, species and environmental conditions. Toxins 14(10): 679, DOI:10.3390/toxins14100679

R.C.Cruz, P.R. Costa, S. Vinga, L. Krippahl, M.B. Lopes (2021). A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. Journal of Marine Science and Engineering 9 (3): 283, DOI:10.3390/jmse9030283

J.L. Serras, S. Vinga, A.M. Carvalho (2021). Outlier detection for multivariate time series using dynamic Bayesian networks. Applied Sciences 11(4):1955. DOI:10.3390/app11041955

Papers in conference proceedings

D. Ribeiro, F. Ferraz, M.B. Lopes, S. Rodrigues, P.R. Costa, S. Vinga, A.M. Carvalho (2023). Causal graph discovery for explainable insights on marine biotoxin shellfish contamination. In: P. Quaresma, D. Camacho, H. Yin, T. Gonçalves, V. Julian, A.J. Tallón-Ballesteros (eds). Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham.

F. Ferraz, D. Ribeiro, M.B. Lopes, S. Pedro, S. Vinga, A.M. Carvalho (2023). Comparative analysis of machine learning models for time-series forecasting of Escherichia coli contamination in Portuguese shellfish production areas. The 9th Annual Conference on machine Learning, Optimization and Data science.

A.R. Baião, C. Peixoto, M.B. Lopes, P.R. Costa, A.M. Carvalho, S. Vinga (2023). Evaluating the causal role of environmental data in shellfish biotoxin contamination on the Portuguese coast. The 22nd Portuguese Conference on Artificial Intelligence.

A. Ferreira, S.C. Madeira, M. Gormicho, M. Carvalho, S. Vinga, A.M. Carvalho (2021). Predictive Medicine Using Interpretable Recurrent Neural Networks. In Del Bimbo A. et al. (eds), Pattern Recognition. ICPR International Workshops and Challenges (ICPR´21). Lecture Notes in Computer Science, Vol. 12661. Springer, Cham, pp. 187-202. DOI: 10.1007/978-3-030-68763-2_14

Cardoso, J. Estima, B. Martins (2020). Spatio-temporal forecasting with gridded remote sensing data using feed-backward decoding. Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 393–396, DOI:10.1145/3397536.3422247T

Anacleto, S. Vinga, A.M. Carvalho (2020). MSAX: Multivariate symbolic aggregate approximation for time series classification. In Cazzaniga P., Besozzi D., Merelli I., Manzoni L., editors, Proceedings of the 16th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB´19), Lecture Notes in Computer Science, Vol. 12313. Springer, Cham, pp. 90-97. 2020. Revised Selected Papers. DOI:10.1007/978-3-030-63061-4_9

Master Dissertations

Evaluating the role of environmental variables on shellfish biotoxin contamination via supervised learning

Manuel Oliveira, Computer Science, NOVA School of Science and Technology, NOVA University of Lisbon

Comparative analysis of machine learning models for time-series forecasting of Escherichia coli contamination in Portuguese shellfish production areas

Filipe Ferraz, Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon

Graph causal discovery for explainable insights on marine biotoxin shellfish contamination

Diogo Ribeiro, Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon

Development of a deep learning-based tool to predict shellfish contamination by marine biotoxins

Daniela Trindade, Analysis and Engineering of Big Data, NOVA School of Science and Technology , NOVA University of Lisbon

A machine learning approach to Sentinel-3 imagery feature extraction

João Costa, Computer Science, NOVA School of Science and Technology , NOVA University of Lisbon

Forecasting shellfish contamination by marine biotoxins based on multivariate time series

Rafaela Cruz, Analysis and Engineering of Big Data, NOVA School of Science and Technology, NOVA University of Lisbon

Multivariate time-series modeling of shellfish contamination with Dynamic Bayesian Networks

Michael Madeira, Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon

Time-series analysis and forecasting of shellfish contamination and safety

André Pereira, Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon

Forecasting marine biotoxins in bivalve molluscs based on machine learning

Lirio Ramalheira, Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon