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Detection of vessel anomalies: A Bayesian network approach
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).ORCID iD: 0000-0001-8884-2154
2007 (English)In: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), IEEE Computer Society, 2007, p. 395-400Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2007. p. 395-400
National Category
Computer Sciences
Research subject
Technology
Identifiers
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
Conference
2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP; Melbourne, VIC; Australia; 3 December 2007 through 6 December 2007
Available from: 2008-10-23 Created: 2008-10-23 Last updated: 2018-01-13Bibliographically approved

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Johansson, FredrikFalkman, Göran

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf