In this study artificial neural networks were used for elaboration of the new method of monitoring of excreted nanocomposites-drug carriers and their components in human urine by their fluorescence spectra. The problem of classification of nanocomposites consisting of fluorescence carbon dots covered by copolymers and ligands of folic acid in urine was solved. A set of different architectures of neural networks and 4 alternative procedures of the selection of significant input features: by cross-correlation, cross-entropy, standard deviation and by analysis of weights of a neural network were used. The best solution of the problem of classification of nanocomposites and their components in urine provides the perceptron with 8 neurons in a single hidden layer, trained on a set of significant input features selected using cross-correlation. The percentage of correct recognition averaged over all five classes, is 72.3%.
|Title of host publication
|First International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures
|Samsonovich Alexei V, Klimov Valentin V.
|Published - 2018
|MoE publication type
|A4 Article in a conference publication
|International Early Research Career Enhancement School on BICA and Cybersecurity - First International Early Research Career Enhancement School on BICA and Cybersecurity (FIERCES 2017)
Duration: 1 Jan 2018 → …
|International Early Research Career Enhancement School on BICA and Cybersecurity
|01/01/18 → …