Zebra mussels' behaviour detection, extraction and classification using wavelets and kernel methods

Piotr Przymus, Krzysztof Rykaczewski, Ryszard Wiśniewski

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper concerns the detection, feature extraction and classification of behaviours of Dreissena polymorpha. A new algorithm based on wavelets and kernel methods that detects relevant events in the collected data is presented. This algorithm allows us to extract elementary events from the behaviour of a living organism. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions.

Original languageEnglish
Pages (from-to)81-89
Number of pages9
JournalFuture Generation Computer Systems
Volume33
DOIs
StatePublished - Apr 2014
Externally publishedYes

Keywords

  • BEWS
  • Classification
  • Feature extraction
  • Wavelet
  • Zebra mussel

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