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

Munir, Mohsin; Siddiqui, Shoaib Ahmed; Chattha, Muhammad Ali; Dengel, Andreas; Ahmed, Sheraz

Munir, Mohsin; Siddiqui, Shoaib Ahmed; Chattha, Muhammad Ali; Dengel, Andreas; Ahmed, Sheraz.

Afiliación

Munir M; German Research Center for Artificial Intelligence (DFKI) GmbH, 67663 Kaiserslautern, Germany. mohsin.munir@dfki.de.
Siddiqui SA; Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany. mohsin.munir@dfki.de.
Chattha MA; German Research Center for Artificial Intelligence (DFKI) GmbH, 67663 Kaiserslautern, Germany. shoaib_ahmed.siddiqui@dfki.de.
Dengel A; Fachbereich Informatik, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany. shoaib_ahmed.siddiqui@dfki.de.
Ahmed S; German Research Center for Artificial Intelligence (DFKI) GmbH, 67663 Kaiserslautern, Germany. muhammad_ali.chattha@dfki.de.

Sensors (Basel) ; 19(11)2019 May 29.

Article en En | MEDLINE | ID: mdl-31146357

Palabras clave

anomaly detection; deep neural networks; model fusion; sensor data; statistical models; time-series analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2019 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Suiza

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