RipNet: a lightweight one-class deep neural network for the identification of RIP currents

Rashid, Ashraf Haroon, Razzak, Imran, Tanveer, M and Robles-Kelly, Antonio 2020, RipNet: a lightweight one-class deep neural network for the identification of RIP currents, in ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020, Springer, Cham, Switzerland, pp. 172-179, doi: 10.1007/978-3-030-63823-8_21.

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Title RipNet: a lightweight one-class deep neural network for the identification of RIP currents
Author(s) Rashid, Ashraf Haroon
Razzak, ImranORCID iD for Razzak, Imran orcid.org/0000-0002-3930-6600
Tanveer, M
Robles-Kelly, AntonioORCID iD for Robles-Kelly, Antonio orcid.org/0000-0002-2465-5971
Conference name Neural Information Processing. International Conference (27th : 2020 : Online from Bangkok, Thailand)
Conference location Online from Bangkok, Thailand
Conference dates 2020/11/18 - 2020/11/22
Title of proceedings ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020
Editor(s) Yang, H
Pasupa, K
Leung, AC-S
Kwok, JT
Chan, JH
King, I
Publication date 2020
Series Neural Information Processing International Conference
Start page 172
End page 179
Total pages 8
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Rip currents
Rip detection
CNN
Autoencoder
Anomaly detection
Notes This conference was originally scheduled to be held in Bangkok, Thailand, however due to the 2020 COVID Pandemic, the event was held online.
ISBN 9783030638221
ISSN 1865-0929
1865-0937
Language eng
DOI 10.1007/978-3-030-63823-8_21
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146505

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