Problems of indirect measurements arising in experimental science belong to the class of inverse problems (IP), which are often ill-posed, non-linear, and have high dimensionality. Machine learning methods are used to solve IP due to their ability to resist these unfavorable properties. In this study, we solve an IP of determination of concentrations of components in multi-component solutions by their Raman spectra. The results demonstrated by various machine learning methods are compared by the solution error and by their resilience to various types of noise encountered in experimental spectroscopy. The best results were demonstrated by multi-layer perceptrons with two hidden layers and by convolutional neural networks with one convolutional layer. For multiplicative noise with high noise level, the most noise resilient algorithms were random forest and gradient boosting.
Wavelet transformation uses a special basis widely known for its unique properties, the most important of which are its compactness and multi-resolution analysis of original signal. However, for a standard discrete and continuous wavelet transform (CWT), the extracted set of feature may be not optimal for solving given inverse problem. If no inverse transformation is needed, the values of transition and dilation coefficients may be determined during network training, and the windows corresponding to various wavelet functions may overlap. In this study, we suggest Adaptive Window Wavelet Neural Network (AWWNN) with bottom to top strategy of iterative neighboring windows merging, designed primarily for signal processing. The efficiency of proposed algorithm was compared on the example of the inverse problem (IP) of Raman spectroscopy of complex solutions of inorganic salts. The IP was solved using a dense neural network based on features generated using the proposed approach and a standard CWT.
In this study we propose a new approach to monitoring of the removal of luminescent nanocomposites and their components with urine using artificial neural networks. A complex multiparametric problem of optical imaging of synthesized nanocomposites - nanometer graphene oxides, covered by the poly(ethylene imine)–poly(ethylene glycol) copolymer and by the folic acid in a biomaterial is solved. The proposed method is applicable for optical imaging of any fluorescent nanoparticles used as imaging nanoagents in biological tissue.
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