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Detection of vessel anomalies: A Bayesian network approach
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Högskolan i Skövde, Skövde Artificial Intelligence Lab (SAIL).
Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Skövde Artificial Intelligence Lab (SAIL).ORCID-id: 0000-0001-8884-2154
2007 (engelsk)Inngår i: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), IEEE Computer Society, 2007, s. 395-400Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

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IEEE Computer Society, 2007. s. 395-400
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URN: urn:nbn:se:his:diva-2312DOI: 10.1109/ISSNIP.2007.4496876ISI: 000255633800067Scopus ID: 2-s2.0-51349156680ISBN: 1-4244-1502-0 ISBN: 978-1-4244-1502-1 ISBN: 978-1-4244-1501-4 OAI: oai:DiVA.org:his-2312DiVA, id: diva2:113678
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2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP; Melbourne, VIC; Australia; 3 December 2007 through 6 December 2007
Tilgjengelig fra: 2008-10-23 Laget: 2008-10-23 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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Johansson, FredrikFalkman, Göran

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Totalt: 192 treff
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