Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
425014 | Future Generation Computer Systems | 2014 | 9 Pages |
•We have developed a fully automated method for extraction and analysis of the behaviour of Dreissena polymorpha.•We have evaluated usefulness of the feature set used for classification.•We have proposed a framework for the classification of control and stress conditions for the purpose of the risk analysis.
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.