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Machine Learning for SPR PCF Sensors by Paolo Barattini is a document available to read on EtoBox.

What is Machine Learning for SPR PCF Sensors about?

This conference paper presents a machine learning approach for optimizing the geometry of the plasmonic layer in surface plasmon resonance photonic crystal fiber sensors. By combining linear regression and gradient boosted regression algorithms, the study achieves high predictive accuracy with an R² value exceeding 0.97 and a low margin of error. The results indicate a robust method for enhancing sensor performance through precise geometric parameter predictions.

Author

Paolo Barattini

Language

EN