MATEC Web Conf.
Volume 342, 20219th edition of the International Multidisciplinary Symposium “UNIVERSITARIA SIMPRO 2021”: Quality and Innovation in Education, Research and Industry – the Success Triangle for a Sustainable Economic, Social and Environmental Development”
|Number of page(s)||7|
|Section||Developments in Systems Control, Information Technology and Cybersecurity|
|Published online||20 July 2021|
Automatic identification of hallucinogenic amphetamines based on their ATR-FTIR spectra processed with Convolutional Neural Networks
”Dunărea de Jos” University of Galati, Faculty of Science and Environment, Domnească Street, 47, RO-800008, Galati, Romania
* Corresponding author: Mirela Praisler, Mirela.Praisler@ugal.ro
New psychoactive drugs that are leading to severe intoxications are constantly seized on the European black market. Recent studies indicate that most of these new substances are synthetic cannabinoids and hallucinogenic amphetamines. In this study, we are presenting the results obtained with an expert system that was built to identify automatically the class identity of these types of drugs of abuse, based on their Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectra processed with Convolutional Neural Networks (CNNs). CNNs have been applied with great success in recent years in various computer applications, such as image classification, but little work has been done in using this kind of deep learning models for spectral data classification. The aim of this study was to improve the detection accuracy (classification performance) that we have already obtained with other statistical mathematics and artificial intelligence techniques. The performances of the CNN system are discussed in comparison with those of the later models.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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