dLSTM: a new approach for anomaly detection using deep learning with delayed prediction
Crossref DOI link: https://doi.org/10.1007/s41060-019-00186-0
Published Online: 2019-05-15
Published Print: 2019-09
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Maya, Shigeru
Ueno, Ken
Nishikawa, Takeichiro
Text and Data Mining valid from 2019-05-15
Version of Record valid from 2019-05-15
Article History
Received: 24 May 2017
Accepted: 27 April 2019
First Online: 15 May 2019