文献
J-GLOBAL ID:202102287453331999
整理番号:21A2571756
Tri-Axial加速度計を用いたPort Jackson Shark挙動を同定するための機械学習モデルの評価【JST・京大機械翻訳】
Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers
著者 (5件):
Kadar Julianna P.
(Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia)
,
Ladds Monique A.
(Marine Ecosystems Team, Wellington University, Wellington 6012, New Zealand)
,
Day Joanna
(Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Sydney, NSW 2088, Australia)
,
Lyall Brianne
(Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Veterinary Centre, Midlothian EH25 9RG, UK)
,
Brown Culum
(Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia)
資料名:
Sensors (Web)
(Sensors (Web))
巻:
20
号:
24
ページ:
7096
発行年:
2020年
JST資料番号:
U7015A
ISSN:
1424-8220
CODEN:
SENSC9
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
スイス (CHE)
言語:
英語 (EN)