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We propose an expertise-embedded approach for failure management of optical modules in OTN that incorporates expert decision-making logic into data-driven ML models, thereby enhancing inference capabilities. Empirical assessments reveal a marked performance enhancement in models post-embedding, particularly in few-shot failure scenarios.
© 2024 The Author(s)
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