TY - JOUR
T1 - Artificial neural networks for NAA
T2 - proof of concept on data analysed with k0-based software
AU - Barradas, N. Pessoa
AU - Farjallah, N.
AU - Vieira, A.
AU - Blaauw, M.
PY - 2022
Y1 - 2022
N2 - Artificial intelligence methods such as artificial neural networks, Bayesian networks, genetic algorithms, and others, have shown great potential for application, not only as classification schemes, but also in numerical data analysis. In this work, we explore how, from a limited number of spectra (around 200), an ANN could be efficiently developed, using data augmentation techniques and optimized architecture, and used to analyse neutron activation analysis (NAA) data. The IAEA Collaborating Centre Research Institute Delft (RID), Netherlands, has collected NAA data sets consisting of one single spectrum per sample to determine one single element (selenium), with addition of a marker (caesium) for flux normalization, all irradiated and measured the exact same way and analysed with k0-based software. The problem studied is one of the simplest that can be addressed with NAA; therefore the present work is intended merely as proof of concept that ANNs can perform well in NAA data analysis of simple problems. We present the results and discuss how to extend the present work to more demanding problems in NAA.
AB - Artificial intelligence methods such as artificial neural networks, Bayesian networks, genetic algorithms, and others, have shown great potential for application, not only as classification schemes, but also in numerical data analysis. In this work, we explore how, from a limited number of spectra (around 200), an ANN could be efficiently developed, using data augmentation techniques and optimized architecture, and used to analyse neutron activation analysis (NAA) data. The IAEA Collaborating Centre Research Institute Delft (RID), Netherlands, has collected NAA data sets consisting of one single spectrum per sample to determine one single element (selenium), with addition of a marker (caesium) for flux normalization, all irradiated and measured the exact same way and analysed with k0-based software. The problem studied is one of the simplest that can be addressed with NAA; therefore the present work is intended merely as proof of concept that ANNs can perform well in NAA data analysis of simple problems. We present the results and discuss how to extend the present work to more demanding problems in NAA.
KW - Artificial Intelligence
KW - Artificial neural networks
KW - Neutron activation analysis
KW - Nuclear analytical techniques
UR - http://www.scopus.com/inward/record.url?scp=85139238441&partnerID=8YFLogxK
U2 - 10.1007/s10967-022-08568-8
DO - 10.1007/s10967-022-08568-8
M3 - Article
AN - SCOPUS:85139238441
SN - 0236-5731
VL - 332
SP - 3421
EP - 3429
JO - Journal of Radioanalytical and Nuclear Chemistry
JF - Journal of Radioanalytical and Nuclear Chemistry
IS - 8
ER -
