A TinyML approach to non-repudiable anomaly detection in extreme industrial environments

Research article (2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), 2022) · cited 24× · AI/ML
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A TinyML approach to non-repudiable anomaly detection in extreme industrial environments

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A TinyML approach to non-repudiable anomaly detection in extreme industrial environments is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). A TinyML approach to non-repudiable anomaly detection in extreme industrial environments. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-tinyml-approach-to-non-repudiable-anomaly-detection-in-extreme-industrial-environments
MLA “A TinyML approach to non-repudiable anomaly detection in extreme industrial environments.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-tinyml-approach-to-non-repudiable-anomaly-detection-in-extreme-industrial-environments.
BibTeX @misc{4ortxyz_a-tinyml-approach-to-non-repudiable-anomaly-detection-in-extreme-industrial-environments_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A TinyML approach to non-repudiable anomaly detection in extreme industrial environments}}, year = {2026}, url = {https://4ort.xyz/entity/a-tinyml-approach-to-non-repudiable-anomaly-detection-in-extreme-industrial-environments}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A TinyML approach to non-repudiable anomaly detection in extreme industrial environments — https://4ort.xyz/entity/a-tinyml-approach-to-non-repudiable-anomaly-detection-in-extreme-industrial-environments (retrieved 2026-05-24)

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