FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with...

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Detalles Bibliográficos
Autores principales: Munir, Mohsin, Siddiqui, Shoaib Ahmed, Chattha, Muhammad Ali, Dengel, Andreas, Ahmed, Sheraz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603659/
https://www.ncbi.nlm.nih.gov/pubmed/31146357
http://dx.doi.org/10.3390/s19112451

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603659/
https://www.ncbi.nlm.nih.gov/pubmed/31146357
http://dx.doi.org/10.3390/s19112451

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