Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challe...

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Bibliographic Details
Main Authors:	Robin Schiemer, Matthias Rüdt, Jürgen Hubbuch
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-01-01
Series:	Frontiers in Bioengineering and Biotechnology
Subjects:	chemometrics convolutional neural networks process analytical technology data augmentation hyperparameter optimization feature importance
Online Access:	https://www.frontiersin.org/articles/10.3389/fbioe.2024.1228846/full

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