Items related to Machine Learning in Aquaculture: Hunger Classification...
Synopsis
1. Introduction
1.1 Fish hunger behaviour1.2 Image processing parameters
1.3 Machine learning in fish behaviour
2.0 Monitoring and feeding integration of demand feeder systems
2.1 Overview
2.2 Fish monitoring setup
2.3 Fish Growth
2.4 Results and discussion
3. Image processing features extraction on fish behaviour
3.1 Overview3.2 Data pre-processing
3.3 Clustering
3.4 Feature extraction and classification
3.5 Results and discussion
4. Time-series identification of fish feeding behaviour
4.1 Overview
4.2 Event identification
4.3 PCA based feature selection
4.4 Classification accuracy
4.5 Results and discussion
5. Hyperparameter tuning for hunger state classification
5.1 Overview5.2 Optimization of Bayesian
5.3 Classification accuracy
5.4 Result and discussion
6.0 Concluding remarks
"synopsis" may belong to another edition of this title.
- PublisherSpringer
- Publication date2020
- ISBN 10 9811522383
- ISBN 13 9789811522383
- BindingPaperback
- LanguageEnglish
- Number of pages68
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