Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering

M. Hamed Mozaffari; Li-Lin Tay

doi:10.1117/12.2576447

8 November 2020 Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering

M. Hamed Mozaffari, Li-Lin Tay

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115250S (2020) https://doi.org/10.1117/12.2576447
Event: SPIE Future Sensing Technologies, 2020, Online Only

Abstract

An accurate supervised classification technique requires a large training database with an equal number of samples in each category. However, in practice, data class imbalance is naturally inherent in detection and identification tasks. In an extreme case, one category of data has a majority of training samples (positive class), causing over-classifying. In these circumstances, the negative classes are either absent, poorly sampled or not well defined. Deep one-class classifiers are artificial neural networks developed to overfit the positive class samples. This unique situation constrains the network model to be trained data features just with the knowledge of the positive class. One well-known application of one-class classifiers is for anomaly detection problem, where the model stands out outliers. In this study, we proposed using a one-dimensional CNN model for anomaly detection of Surface-Enhanced Raman Spectra applicable for Portable Raman Spectrometer in field investigations.

Conference Presentation

Citation Download Citation

M. Hamed Mozaffari and Li-Lin Tay "Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115250S (8 November 2020); https://doi.org/10.1117/12.2576447

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