Comparing End-to-End Machine Learning Methods for Spectra Classification

Comparing End-to-End Machine Learning Methods for Spectra Classification

In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved st...

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Bibliographic Details
Main Authors: Yue Sun, Sandor Brockhauser, Péter Hegedűs
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Applied Sciences
Subjects:
end-to-end machine learning
spectra classification
deep learning (DL)
convolutional neural network (CNN)
transformer
long short-term memory (LSTM)
Online Access:https://www.mdpi.com/2076-3417/11/23/11520

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https://www.mdpi.com/2076-3417/11/23/11520

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