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

Piotr Przymus; Krzysztof Rykaczewski; Ryszard Wiśniewski

doi:10.1016/j.future.2013.04.015

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

Piotr Przymus, Krzysztof Rykaczewski, Ryszard Wiśniewski

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	81-89
Number of pages	9
Journal	Future Generation Computer Systems
Volume	33
DOIs	https://doi.org/10.1016/j.future.2013.04.015
State	Published - Apr 2014
Externally published	Yes

Keywords

BEWS
Classification
Feature extraction
Wavelet
Zebra mussel

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.future.2013.04.015

Cite this

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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.",

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KW - BEWS

KW - Classification

KW - Feature extraction

KW - Wavelet

KW - Zebra mussel

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

Abstract

Keywords

UN SDGs

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