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Analysis of infrared spectra using deep neural networks

Tina Avbelj (2016) Analysis of infrared spectra using deep neural networks. EngD thesis.

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    Absorption spectra obtained from the sample irradiated by infrared radiation represent a very useful method for observing the chemical composition of different kinds of samples, from cell tissue to various materials. For spectrum analysis, we often use classification algorithms. A suitable algorithm for this task is an artificial neural network. In the diploma thesis, we explored the usefulness of artificial neural networks for classification of infrared spectra. We measured classification accuracies on different data sets and compared them to the results of support vector machines and multinomial logistic regression. We also examined the performance of convolutional neural networks. The results achieved by the artificial neural networks were promising. However, they were not significantly better than those of the support vector machines. On the other hand, the performance of the latter was considerably faster.

    Item Type: Thesis (EngD thesis)
    Keywords: artificial neural networks, deep learning, infrared spectra
    Number of Pages: 46
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Janez Demšar257Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537164483)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3572
    Date Deposited: 13 Sep 2016 14:31
    Last Modified: 03 Oct 2016 14:14
    URI: http://eprints.fri.uni-lj.si/id/eprint/3572

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