Variational restricted Boltzmann machines to automated anomaly detection-Reference-Cited by-同舟云学术

Variational restricted Boltzmann machines to automated anomaly detection

Published:2022-03-01 Issue:18 Volume:34 Page:15207-15220
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Demertzis Konstantinos^ORCID,Iliadis Lazaros,Pimenidis Elias,Kikiras Panagiotis

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07060-4.pdf

Reference68 articles.

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3. Radanliev P et al (2020) Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains. [Online]. https://www.preprints.org/manuscript/201903.0123/v2. Accessed 19 Jan 2021

4. Demertzis K, Iliadis L, Tziritas N, Kikiras P (2020) Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput Appl 32(23):17361–17378. https://doi.org/10.1007/s00521-020-05189-8

5. Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. [Online]. Preprint http://arxiv.org/abs/1901.03407. Accessed 08 Sep 2021

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