Marques, J.G., Guedes, L.A., & Da Costa Abreu, M. (2022). Evaluating time influence over performance of machine-learning-based diagnosis: a case study of Covid-19 Pandemic in Brazil. International Journal of Environmental Research and Public Health, 20 (1). http://doi.org/10.3390/ijerph20010136

Nascimento, F., Cavalcanti, G., & Da Costa Abreu, M. (2022). Unintended bias evaluation: an analysis of hate speech detection and gender bias mitigation on social media using ensemble learning. Expert Systems with Applications, 201. http://doi.org/10.1016/j.eswa.2022.117032

Dos Santos Nascimento, F.R., Smith, S., & Da Costa Abreu, M. (2022). Exploring Medieval Manuscripts Writer Predictability: A Study on Scribe and Letter Identification. Digital Studies/le champ numérique, 12 (1). http://doi.org/10.16995/dscn.8096

De Lima, T.A., & Da Costa Abreu, M. (2022). Phoneme Analysis for Multiple Languages with Fuzzy-Based Speaker Identification. IET Biometrics. http://doi.org/10.1049/bme2.12078

de Lima, T.A., & Da Costa Abreu, M. (2022). Phoneme analysis for multiple languages with fuzzy‐based speaker identification. IET Biometrics. http://doi.org/10.1049/bme2.12078

Da Costa Abreu, M., & Silva, B. (2020). A critical analysis of ’Law 4.0’: The use of Automation and Artificial Intelligence and their impact on the judicial landscape of Brazil. Revista de Direitos Fundamentais e Tributação, 1 (3), 1-16. http://doi.org/10.47319/rdft.v1i3.30

Goncalves de A. S. Marques, J.C., Lima Do Nascimento, T.M., Vasiljevic, B., Alves dos Santos Santana, L.E., & Da Costa Abreu, M. (2020). An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach. Journal of the Brazilian Computer Society, 26 (1), 8. http://doi.org/10.1186/s13173-020-00102-6

da Silva, R.S., Da Costa Abreu, M., & Smith, S. (2020). Investigating the use of an ensemble of evolutionary algorithms for letter identification in tremulous medieval handwriting. Evolutionary Intelligence. http://doi.org/10.1007/s12065-020-00427-3

Aguiar de Lima, T., & da Costa-Abreu, M. (2019). A survey on Automatic Speech Recognition systems for Portuguese language and its variations. Computer Speech & Language, 101055. http://doi.org/10.1016/j.csl.2019.101055

Silva, J., Kreutz, M., Pereira, M., & Da Costa Abreu, M. (2019). An investigation of latency prediction for NoC-based communication architectures using machine learning techniques. Journal of Supercomputing, 1-19. http://doi.org/10.1007/s11227-019-02971-x

Da Costa Abreu, M., & Bezerra, G.S. (2019). FAMOS: a framework for investigating the use of face features to identify spontaneous emotions. Pattern Analysis and Applications, 22 (2), 683-701. http://doi.org/10.1007/s10044-017-0675-y

Da Costa Abreu, M.C., & Fairhurst, M. (2011). Enhancing Identity Prediction Using a Novel Approach to Combining Hard- and Soft-Biometric Information. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41 (5), 599-607. http://doi.org/10.1109/tsmcc.2010.2056920

Da Costa Abreu, M.C., & Fairhurst, M. (2009). Analyzing the Benefits of a Novel Multiagent Approach in a Multimodal Biometrics Identification Task. IEEE Systems Journal, 3 (4), 410-417. http://doi.org/10.1109/jsyst.2009.2035978

Fairhurst, M.C., & Abreu, M.C.C. (2009). Balancing performance factors in multisource biometric processing platforms. IET Signal Processing, 3 (4), 342-351. http://doi.org/10.1049/iet-spr.2008.0140

Canuto, A.M.P., Campos, A.M.C., Bezerra, V.M.S., & Abreu, M.C.D.C. (2007). Investigating the use of a multi-agent system for knowledge discovery in databases. International Journal of Hybrid Intelligent Systems, 4 (1), 27-38. http://doi.org/10.3233/his-2007-4104

Canuto, A.M.P., Abreu, M.C.C., de Melo Oliveira, L., Xavier, J.C., & Santos, A.D.M. (2007). Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognition Letters, 28 (4), 472-486. http://doi.org/10.1016/j.patrec.2006.09.001

Canuto, A.M.P., Fagundes, D., Abreu, M.C.C., & Junior, J.C.X. (2006). Using weighted dynamic classifier selection methods in ensembles with different levels of diversity. International Journal of Hybrid Intelligent Systems, 3 (3), 147-158. http://doi.org/10.3233/his-2006-3303

Canuto, A.M., Santos, A.M., Abreu, M.C., Bezerra, V.M., Souza, F.M., & Gomes Junior, M.F. (2004). Investigating the Use of an Agent-Based Multi-classifier System for Classification Tasks. . http://doi.org/10.1007/978-3-540-30499-9_131

Conference papers

Dubinko, N., Bayerl, P.S., Da Costa Abreu, M., & Gibson, H. (2024). Analysing Emotional and Topical Patterns in Conspiracy Theory Narratives: a Discourse Comparative Study on the 2023 Hawaii Wildfires. 2024 14th International Conference on Pattern Recognition Systems (ICPRS), 1-7. http://doi.org/10.1109/icprs62101.2024.10677804

Da Costa Abreu, M., Lycaena Débora, J., Jane, W., Kalinka, B., & Ricardo, M. (2024). Wellness impact of PGR students during COVID-19: A comparative analysis between SHU and USP. In UKCGE Annual Conference 2024, UCL East, Stratford, London, 4 July 2024 - 5 July 2024.

Aguiar, T., & Da Costa Abreu, M. (2023). Automatic collection of transcribed speech for low resources languages. 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS). http://doi.org/10.1109/icprs58416.2023.10179033

Silva, B.S.F., & Da Costa Abreu, M. (2022). Exploring bias analysis on judicial data using machine learning techniques. In 12th International Conference on Pattern Recognition Systems (ICPRS 2022). IEEE: http://doi.org/10.1109/ICPRS54038.2022.9854068

Ajao, O., Garg, A., & Da Costa Abreu, M. (2022). Exploring content-based and meta-data analysis for detecting fake news infodemic: a case study on COVID-19. In 12th International Conference on Pattern Recognition Systems (ICPRS 2022). IEEE: http://doi.org/10.1109/ICPRS54038.2022.9854058

Rannow Budke, J., & Da Costa Abreu, M. (2021). Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021 (pp. 121-126). IET: http://doi.org/10.1049/icp.2021.1433

Nascimento, T.M.L.D., Oliveira, A.V.M.D., Santana, L.E.A.D.S., & Da Costa Abreu, M. (2021). Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021 (pp. 115-120). IET: http://doi.org/10.1049/icp.2021.1446

Rocha de Azevedo Santos, L., Silla Jr., C., & Da Costa Abreu, M. (2021). A methodology for procedural piano music composition with mood templates using genetic algorithms. In 11th International Conference on Pattern Recognition Systems, Curico, Chile, 17 March 2021 - 19 March 2021 (pp. 1-6). IET: http://doi.org/10.1049/icp.2021.1435

Lima do Nascimento, T.M., Alves dos Santos Santana, L.E., & Da Costa Abreu, M. (2021). Fake News on the Covid-19 outbreak: a new metadata-based dataset for the analysis of Brazilian and British Twitter posts. In e Eduardo Souto, M.A.H. (Ed.) The 21th Brazilian Symposium on Information and Computer Systems Security (SBSeg 2021), Virtual, 4 October 2021 - 7 October 2021 (pp. 397-402). SBSeg: http://doi.org/10.5753/sbseg.2021.17332

de Lima, T.A., & da Costa-Abreu, M. (2021). Investigating the use of multiple languages for crisp and fuzzy speaker identification. IET Conference Publications, 2021 (2021), 19-24. http://doi.org/10.1049/icp.2021.1431

Araujo De Souza, G., & Da Costa Abreu, M. (2020). Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata. In IEEE World Congress on Computational Intelligence (IEEE WCCI), Glasgow, UK, 19 July 2020 - 24 July 2020. IEEE: http://doi.org/10.1109/IJCNN48605.2020.9207652

Cavalcante Bandeira, D.R., De Paula Canuto, A.M., Da Costa Abreu, M., Fairhurst, M., Li, C., & Nascimento, D.S.C. (2019). Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction. Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019, 126-131. http://doi.org/10.1109/BRACIS.2019.00031

Da Costa Abreu, M., & Bruno, S. (2019). An empirical analysis of Brazilian courts law documents using learning techniques. In XIX Brazilian Symposium on Information and Computational Systems Security, São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-7). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/198728.pdf

Lima do Nascimento, T.M., Monteiro de Oliveira, A.V., Da Costa Abreu, M., & Oliveira, L. (2019). An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics. In 19th Brazilian Symposium on Information and Computer System Security (SBSeg 2019), São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-4). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/195899.pdf

Da Costa Abreu, M., & Goncalve, J.C. (2019). An evaluation of a three-modal hand-based database to forensic-based gender recognition. In 19th Brazilian Symposium on Information and Computer System Security (SBSeg 2019), São Paulo, Brazil, 2 September 2019 - 5 September 2019 (pp. 1-6). Brazilian Computer Society (SBC): https://sbseg2019.ime.usp.br/anais/195970.pdf

Da Costa Abreu, M., & Pereira, M. (2019). Women in Resistance: Reporting the impact of IEEEWiEUFRNexhibition at Science and Tech Week at UFRN. In 39th Brazilian Computer Society Congress (CSBC 2019), Belem, Brazil, 14 July 2019 - 18 July 2019 (pp. 99-103). Brazilian Computer Society (SBC): http://doi.org/10.5753/wit.2019.6717

Da Costa Abreu, M., & Silveira Silva, R. (2018). Smart Rescue Drones to Find Snowslide Victims. In 18th Brazilian Symposium on Information and Computational Systems Security (SBSeg 2018), Natal, Brazil, 22 October 2018 - 25 October 2018 (pp. 181-184). Brazilian Computer Society (SBC): https://sol.sbc.org.br/index.php/sbseg_estendido/article/view/4155/4084

Da Silva, V.R., & Da Costa Abreu, M. (2018). An empirical biometric-based study for user identification with different neural networks in the online game League of Legends. Proceedings of the International Joint Conference on Neural Networks, 2018-J (2018-J). http://doi.org/10.1109/IJCNN.2018.8489164

Da Silva, V.R., De AraújoSilva, J.C.G., & Da Costa Abreu, M. (2018). A new Brazilian hand-based behavioural biometrics database: Data collection and analysis. IET Seminar Digest, 2016 (2016). http://doi.org/10.1049/ic.2016.0085

Bittencourt, V.G., Da Costa Abreu, M., Souto, M.C.P.D., Costa, J.A.F., & Canuto, A.M.P. (2016). Aplicação de Multiclassificadores Heterogêneos no Reconhecimento de Classes Estruturais de Proteínas. Anais do 7. Congresso Brasileiro de Redes Neurais. http://doi.org/10.21528/cbrn2005-186

Da Silva Beserra, I., Camara, L., & Da Costa Abreu, M. (2016). Using keystroke and mouse dynamics for user identification in the online collaborative game League of Legends. IET Seminar Digest, 2016 (2016). http://doi.org/10.1049/ic.2016.0076

Erbilek, M., Fairhurst, M., & Da Costa Abreu, M. (2013). Age prediction from iris biometrics. 5th International Conference on Imaging for Crime Detection and Prevention, ICDP 2013. http://doi.org/10.1049/ic.2013.0258

Fairhurst, M., & Da Costa Abreu, M. (2012). Using keystroke dynamics for gender identification in social network environment. 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011). http://doi.org/10.1049/ic.2011.0124

Abreu, M., & Fairhurst, M. (2011). Combining Multiagent Negotiation and an Interacting Verification Process to Enhance Biometric-Based Identification. In Lecture Notes in Computer Science, (pp. 95-105). Springer Berlin Heidelberg: http://doi.org/10.1007/978-3-642-19530-3_9

Fairhurst, M.C., & Abreu, M.C.D.C. (2009). An Investigation of Predictive Profiling from Handwritten Signature Data. 2009 10th International Conference on Document Analysis and Recognition, 1305-1309. http://doi.org/10.1109/icdar.2009.94

Abreu, M., & Fairhurst, M. (2009). Improving forgery detection in off-line forensic signature processing. 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), P6. http://doi.org/10.1049/ic.2009.0234

Abreu, M., & Fairhurst, M. (2009). Improving Identity Prediction in Signature-based Unimodal Systems Using Soft Biometrics. In Lecture Notes in Computer Science, (pp. 348-356). Springer Berlin Heidelberg: http://doi.org/10.1007/978-3-642-04391-8_45

Abreu, M., & Fairhurst, M. (2008). Analyzing the impact of non-biometric information on multiclassifier processing for signature recognition applications. 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, 1-6. http://doi.org/10.1109/btas.2008.4699355

Abreu, M., & Fairhurst, M. (2008). An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification. In Lecture Notes in Computer Science, (pp. 130-139). Springer Berlin Heidelberg: http://doi.org/10.1007/978-3-540-89991-4_14

Canuto, A.M.P., Santana, L.E.A., Abreu, M.C.C., & Xavier, J.C. (2008). An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks. Neurocomputing, 71 (16-18), 3319-3325. http://doi.org/10.1016/j.neucom.2008.01.032

Canuto, A.M.P., & Abreu, M.C.C. (2007). Using Fuzzy, Neural and Fuzzy-Neural Combination Methods in Ensembles with Different Levels of Diversity. In Lecture Notes in Computer Science, (pp. 349-359). Springer Berlin Heidelberg: http://doi.org/10.1007/978-3-540-74690-4_36

Da Costa Abreu, M., & Canuto, A.M.P. (2007). An Experimental Study on the Importance of the Choice of the Ensemble Members in Fuzzy Combination Methods. Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). http://doi.org/10.1109/isda.2007.35

Abreu, M.C.C., & Canuto, A.M.P. (2007). An Experimental Study on the Importance of the Choice of the Ensemble Members in Fuzzy Combination Methods. Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). http://doi.org/10.1109/isda.2007.4389693

Abreu, M.C.C., & Canuto, A.M.P. (2007). Evaluating the Influence of the Choice of the Ensemble Members in Some Fuzzy Combination Methods. 2007 International Joint Conference on Neural Networks, 448-453. http://doi.org/10.1109/ijcnn.2007.4370998

Santana, L.E.O., Canuto, A.M.P., & Abreu, M.C.C. (2006). Analyzing the performance of an agent-based neural system for classification tasks using data distribution among the agents. IEEE International Conference on Neural Networks - Conference Proceedings, 2951-2958. http://doi.org/10.1109/ijcnn.2006.247229

Abreu, M.C.D.C., & Canuto, A.M.P. (2006). Analyzing the benefits of using a fuzzy-neuro model in the accuracy of the NeurAge system: An agent-based system for classification tasks. IEEE International Conference on Neural Networks - Conference Proceedings, 2959-2966. http://doi.org/10.1109/ijcnn.2006.247251

O Santana, L., P Canuto, A., & C Abreu, M. (2006). A Neuro-Fuzzy-Based Agent System with Data Distribution among the Agents for Classification Tasks. 2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06), 27. http://doi.org/10.1109/sbrn.2006.6

Abreu, M.C.C., Canuto, A.M.P., & Santana, L.E.A.S. (2005). A comparative analysis of negotiation methods for a multi-neural agent system. Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 6 pp.. http://doi.org/10.1109/ichis.2005.3

Canuto, A.M.P., Oliveira, L.M., Xavier, J.C., Santos, A.M., & Abreu, M.C.C. (2005). Performance and diversity evaluation in hybrid and non-hybrid structures of ensembles. Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 6 pp.. http://doi.org/10.1109/ichis.2005.87

Bittencourt, V.G., Abreut, M.C.C., de Souto, M.C.P., & de P. Canuto, A.M. An empirical comparison of individual machine learning techniques and ensemble approaches in protein structural class prediction. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, 2, 527-531. http://doi.org/10.1109/ijcnn.2005.1555886

Book chapters

(2017). Age predictive biometrics: predicting age from iris characteristics. In Iris and Periocular Biometric Recognition. (pp. 213-234). Institution of Engineering and Technology: http://doi.org/10.1049/pbse005e_ch10