Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring-Reference-Cited by-同舟云学术

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Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
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Author:

Georgakopoulos Spiros V.¹,Tasoulis Sotiris K.²,Vrahatis Aristidis G.³,Moustakidis Serafeim⁴,Tsaopoulos Dimitrios E.⁵,Plagianakos Vassilis P.²

Affiliation:

1. University of Thessaly,Department of Mathematics,Lamia,Greece,35100

2. University of Thessaly,Department of Computer Science & Biomedical Informatics,Lamia,Greece,35100

3. Ionian University,Department of Informatics,Corfu,Greece,49100

4. AIDEAS OU,Tallinn,Estonia,10117

5. Centre for Research and Technology Hellas,Volos,Greece,38333

Funder

Hellenic Foundation for Research and Innovation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892769.pdf?arnumber=9892769

Reference34 articles.

1. Anomaly detection

2. Visualizing data using t-SNE;van der maaten;Journal of Machine Learning Research,2008

3. Principal component analysis: a review and recent developments

4. Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

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