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Hybrid Machine Learning-Assisted Raman Spectroscopy with Generative Feature Augmentation for Pharmaceutical Identification
Author1, Author2, Author3, Author4, Author5
RAGmachine learningspectroscopypharmaceutical identification
2610.02224
Builder Relevance
1h ago80%
Abstract
This study presents HyMLRaman, a hybrid framework for rapid identification of pharmaceutical residues using Raman spectroscopy and machine learning.
Reality Card
Core Claim
HyMLRaman achieves 96.31% accuracy in identifying six pharmaceutical compounds using a hybrid EfficientNet-B3--SVM configuration.
Method / Result
The hybrid configuration reached a macro-F1 score of 96.36%.
Limitations
The effectiveness of the DDPM-based feature augmentation is classifier-dependent, which may affect reproducibility across different models.
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