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English Information

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Authors
# Name
1 Cáudio Ribeiro(claudiovr@id.uff.br)
2 Aline Paes(alinepaes@ic.uff.br)
3 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 De Vries, G. K. D. and Van Someren, M. (2012). Ma- chine learning for vessel trajectories using compression, alignments and domain kno- wledge. Expert Systems with Applications, 39(18):13426–13439.
2 Dobrkovic, A., Iacob, M.-E., and van Hillegersberg, J. (2015). Using machine learning for unsupervised maritime waypoint discovery from streaming ais data. Proc. of the i-KNOW ’15, pages 1–8.
3 EMSA (2019). Annual overview of marine casualties and incidents. Technical report.
4 Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the KDD’96, page 226–231. AAAI Press.
5 Kowalska, K. and Peel, L. (2012). Maritime anomaly detection using gaussian process active learning. 2012 15th International Conference on Information Fusion, pages 1164–1171. IEEE.
6 Ribeiro,C.V.,Paes,A.,and de Oliveira, D.(2023). Ais-based maritime anomaly traffic detection: A review. Expert Systems with Applications, page 120561.
7 Sidibé, A. and Shu, G. (2017). Study of automatic anomalous behaviour detection techniques for maritime vessels. The journal of Navigation, 70(4):847– 858.
8 Handbook of Statistics. United Nations Con- ference on Trade and Development. Available at https://unctad.org/en/ PublicationsLibrary/tdstat44_en.pdf.
9 Handbook of Statistics. United Nations Conference on Trade and Development. Available at https://unctad.org/system/files/ official-document/tdstat47_en.pdf.
10 Wang, X., Liu, X., Liu, B., de Souza, E. N., and Matwin, S. (2014). Vessel route anomaly detection with hadoop mapreduce. 2014 IEEE international conference on big data (big data), pages 25–30. IEEE.
11 Weng, J., Yang, D., Qian, T., and Huang, Z. (2018). Combining zero- inflated negative binomial regression with mlrt techniques: an approach to evaluating shipping accident casualties. Ocean Engineering, 166:135–144.
12 Zhao, L. and Shi, G. (2019). Maritime anomaly detection using density-based clustering and recurrent neural network. The Journal of Navigation, 72(4):894–916.
13 Zor, C. and Kittler, J. (2017). Maritime anomaly detection in ferry tracks. 2017 IEEE International Conference on Acoustics, Speech and Signal Proces- sing (ICASSP), pages 2647–2651. IEEE.