Convolutional Neural Networks for Spectroscopic Data Analysis
Annotation
Radboud University, 10 oktober 2023
Promotores : Marchiori, E., Buydens, L.M.C. Co-promotores : Laarhoven, T.M. van, Jansen, J.J.
Publication type
Dissertation
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Organization
Data Science
Languages used
English (eng)
Subject
Data ScienceAbstract
In this thesis, we described spectroscopic data and analyzed them with deep learning inspired methodologies. The goal was to design artificial neural network-based methodologies capable of dealing with known challenges of the analysis of spectroscopic data. The main challenges are: 1) a limited number of samples; 2) high number of features; 3) noisy data. The goals are then to be robust against many types of noise affecting the samples and providing means for interpretation while achieving an high classification accuracy. In particular, we focused on developing convolutional neural network-based methodologies for the classification and interpretation of spectroscopic data. There are many types of spectroscopic data having different properties, but all sharing spectral locality: values of neighboring wavelengths or wavenumbers that are not too much dissimilar from each other. We exploited these properties in our approach.
This item appears in the following Collection(s)
- Academic publications [245400]
- Dissertations [13780]
- Electronic publications [132943]
- Faculty of Science [37561]
- Open Access publications [106464]
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