DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Publication Details

Journal
International Journal of Intelligent Systems Technologies and Applications
Published
2025-05-15
DOI
https://doi.org/10.1504/ijista.2026.10071217
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants

K. Meena
International Journal of Intelligent Systems Technologies and Applications
Fault Detection and Control Systems
article

DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants

K. Meena
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Intelligent Systems Technologies and ApplicationsVol. 24(1)
Openalex Percentile: Top 89%
Fault Detection and Control Systems
DOISourceOpenAlex
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DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants — K. Meena · International Journal of Intelligent Systems Technologies and Applications (2025) | TGRS Research Map | TGRS