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- W2521500001 abstract "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." @default.
- W2521500001 created "2016-09-30" @default.
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- W2521500001 date "2016-09-08" @default.
- W2521500001 modified "2023-09-27" @default.
- W2521500001 title "Analysis of infrared spectra using deep neural networks" @default.
- W2521500001 hasPublicationYear "2016" @default.
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