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Development of a comprehensive dual autoencoder and SVDD architecture combining latent representation learning and nonlinear boundary compactness for accurate detection of complex anomalies in large-scale industrial monitoring

Crossref DOI link: https://doi.org/10.1007/s44257-026-00055-8

Published Online: 2026-03-03

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Cheikh, Khamiss

Boudi, EL Mostapha

Rabi, Rabi

Mokhliss, Hamza
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Text and Data Mining valid from 2026-03-03

Version of Record valid from 2026-03-03
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Article History

Received: 23 September 2025

Accepted: 20 January 2026

First Online: 3 March 2026

Declarations

:

: This study did not involve human participants, animals, or personal data. Therefore, ethical approval was not required for this research. Consent to participate was not applicable as no human subjects were involved in this study.

: Consent to publish was not applicable as the manuscript does not contain any individual person’s data in any form.

: The authors declare no competing interests.

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