Abstract
This study presents the results of solving the inverse problem of determining the concentrations of heavy metal ions of multicomponent solutions by their Raman and absorption spectra using integration of machine learning algorithms and integration of optical spectroscopy methods. It is shown that if the integrated methods differ much by their accuracy, then their integration is not effective. This is observed both for algorithmic integration and for integration of physical methods.
This study has been performed at the expense of the Russian Science Foundation, grant no. 19–11-00333, https://rscf.ru/en/project/19-11-00333/.
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Guskov, A., Laptinskiy, K., Burikov, S., Isaev, I. (2023). Integration of Data and Algorithms in Solving Inverse Problems of Spectroscopy of Solutions by Machine Learning Methods. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research VI. NEUROINFORMATICS 2022. Studies in Computational Intelligence, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-031-19032-2_41
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