A machine learning-based multiclass classification model for bee colony anomaly identification using an IoT-based audio monitoring system with an edge computing framework

Author:

Chen Sheng-Hao,Wang Jen-Cheng,Lin Hung-Jen,Lee Mu-Hwa,Liu An-Chi,Wu Yueh-Lung,Hsu Pei-Shou,Yang En-ChengORCID,Jiang Joe-AirORCID

Publisher

Elsevier BV

Reference47 articles.

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3. Amlathe, P. (2018). Standard machine learning techniques in audio beehive monitoring: Classification of audio samples with logistic regression, k-nearest neighbor, random forest, and support vector machine. [Unpublished master’s thesis]. Utah State University, UT, USA. doi: 10.26076/7e6c-25b1.

4. IoT monitoring and prediction modeling of honeybee activity with alarm;Andrijević;Electronics,2022

5. The transmission of deformed wing virus between honeybees (Apis mellifera L.) by the ectoparasitic mite varroa jacobsoni Oud;Bowen-Walker;Journal of Invertebrate Pathology,1999

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