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- W4308331251 abstract "This paper presents a machine-learning approach for detecting swarming events. Three different classification algorithms are tested: The k-Nearest Neighbors algorithm (k-NN) and Support Vector Machine (SVM), and a newly proposed by the authors, U-Net Convolutional Neural Network (CNN), developed for biomedical image segmentation. Next, the authors present their experimental scenario of collecting audio data of swarming and non-swarming events and evaluating the results from the k-NN and SVM classifiers and their proposed CNN algorithm. Finally, the authors compare these three methods and present the cross-comparison results of the optimal method for early and late/close-to-the-event detection of swarming." @default.
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- W4308331251 date "2022-11-03" @default.
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- W4308331251 title "Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound" @default.
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- W4308331251 doi "https://doi.org/10.3390/signals3040048" @default.
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