Putting Together Wavelet-based Scaleograms and Convolutional Neural Networks for Anomaly Detection in Nuclear Reactors

Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence ◽

10.1145/3369114.3369121 ◽

2019 ◽

Author(s):

Thanos Tagaris ◽

George Ioannou ◽

Maria Sdraka ◽

Georgios Alexandridis ◽

Andreas Stafylopatis

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Nuclear Reactors

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Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance

2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) ◽

10.1109/usbereit51232.2021.9455004 ◽

2021 ◽

Author(s):

Garnik Vareldzhan ◽

Kirill Yurkov ◽

Konstantin Ushenin

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Mahalanobis Distance ◽

Center Loss ◽

Image Datasets

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Unsupervised Hyperspectral Anomaly Detection with Convolutional Neural Networks

2021 29th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu53274.2021.9477870 ◽

2021 ◽

Author(s):

Fatma Nur Yilmaz ◽

Sertac Arisoy ◽

Koray Kayabol

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks

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Machine learning-based climate time series anomaly detection using convolutional neural networks

Weather and Climate ◽

10.2307/27031377 ◽

2020 ◽

Vol 40 (1) ◽

pp. 16

Author(s):

Srinivasan ◽

Wang ◽

Bulleid

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Climate Time Series

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Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data

Lecture Notes in Computer Science - Intelligent Data Engineering and Automated Learning – IDEAL 2017 ◽

10.1007/978-3-319-68935-7_54 ◽

2017 ◽

pp. 500-507 ◽

Cited By ~ 2

Author(s):

Oleg Gorokhov ◽

Mikhail Petrovskiy ◽

Igor Mashechkin

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Text Data ◽

Unsupervised Anomaly Detection

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Radio Frequency Classification and Anomaly Detection using Convolutional Neural Networks

2019 IEEE Radar Conference (RadarConf) ◽

10.1109/radar.2019.8835662 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marvin A. Conn ◽

Darsana Josyula

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Radio Frequency ◽

Convolutional Neural Networks

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An Empirical Study on Network Anomaly Detection Using Convolutional Neural Networks

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) ◽

10.1109/icdcs.2018.00178 ◽

2018 ◽

Cited By ~ 17

Author(s):

Donghwoon Kwon ◽

Kathiravan Natarajan ◽

Sang C. Suh ◽

Hyunjoo Kim ◽

Jinoh Kim

Keyword(s):

Neural Networks ◽

Empirical Study ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Network Anomaly Detection

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Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering

SPIE Future Sensing Technologies ◽

10.1117/12.2576447 ◽

2020 ◽

Author(s):

M. Hamed Mozaffari ◽

Li-Lin Tay

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Raman Scattering ◽

Convolutional Neural Networks ◽

Surface Enhanced Raman Scattering ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Enhanced Raman Scattering

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Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Anais do 14. Congresso Brasileiro de Inteligência Computacional ◽

10.21528/cbic2019-7 ◽

2020 ◽

Author(s):

Rodrigo de Paula Monteiro ◽

Carmelo José Albanez Bastos Filho

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Anomaly Detection ◽

Convolutional Neural Networks

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Two Stream Convolutional Neural Networks for Anomaly Detection in Surveillance Videos

Advances in Intelligent Systems and Computing - Smart Computing Paradigms: New Progresses and Challenges ◽

10.1007/978-981-13-9683-0_5 ◽

2019 ◽

pp. 41-48

Author(s):

Adarsh Jamadandi ◽

Sunidhi Kotturshettar ◽

Uma Mudenagudi

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Surveillance Videos

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A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks

2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID) ◽

10.1109/asid50160.2020.9271685 ◽

2020 ◽

Author(s):

Shuyong Liu ◽

Hongrui Jiang ◽

Sizhao Li ◽

Yang Yang ◽

Linshan Shen

Keyword(s):

Neural Networks ◽

Anomaly Detection ◽

Convolutional Neural Networks ◽

Compression Technique

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