Dealing with Distribution Shift in Acoustic Mosquito Datasets
Document Type
Citation
Publication Date
3-23-2023
Abstract
Considered the deadliest animal on the planet, mosquitoes can carry and transmit life-threatening diseases such as malaria, yellow fever, dengue, and dog heartworm. However, these diseases, which affect millions of people worldwide every year [1], can be prevented by identifying places most susceptible to hosting dangerous species of mosquitoes and taking appropriate measures to prevent mosquito bites. With the recent increase in computing power and associated progress made in domains such as computer vision and machine learning, researchers developed solutions for automatically identifying such places as well as the species of mosquitoes they host.
Rights
© Copyright 2023 IEEE - All rights reserved.
Locate the Document
DOI
10.1109/ICMLA55696.2022.00246
Persistent Identifier
https://archives.pdx.edu/ds/psu/39714
Citation Details
Nkouanga, H. Y., & Singh, S. (2022, December). Dealing with Distribution Shift in Acoustic Mosquito Datasets. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1595-1600). IEEE.