Outlier detection in maritime environments using AIS data and deep recurrent architectures-Reference-Cited by-同舟云学术

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Outlier detection in maritime environments using AIS data and deep recurrent architectures

Published:2024-06-26 Issue: Volume: Page:420-427
ISSN:
Container-title:Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments
language:
Short-container-title:

Author:

Maganaris Constantine¹^ORCID,Protopapadakis Eftychios²^ORCID,Doulamis Nikolaos³^ORCID

Affiliation:

1. School Of Rural Surveying and Geoinformatics Engineering, National Technical Universiti of Athens, Greece

2. School of Information Sciences, University of Macedonia, Greece

3. School Of Rural Surveying and Geoinformatics Engineering, National Technical University of Athens, Greece

Funder

Photogrammetry & Geoinformatics of the National Technical University of Athens

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652037.3663916

Reference13 articles.

1. Learning long-term dependencies with gradient descent is difficult

2. Daniel Blauwkamp, Thuy D Nguyen, and Geoffrey G Xie. 2018. Toward a deep learning approach to behavior-based AIS traffic anomaly detection. In Dynamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop, San Juan, PR.

3. Kyunghyun Cho Bart van Merrienboer Dzmitry Bahdanau and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arxiv:1409.1259 [cs.CL]

4. Yarin Gal. 2016. Uncertainty in Deep Learning. Ph. D. Dissertation. University of Cambridge.

5. An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

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