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Neural Network Classification Method for Solution of the Problem of Monitoring The Removal of the Theranostics Nanocomposites from an Organismстатья
Информация о цитировании статьи получена из
Web of Science
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 12 сентября 2019 г.
- Авторы: Olga Sarmanova, Sergey Burikov, Sergey Dolenko, von Haartman Eva, Didem Sen Karaman, Igor Isaev, Kirill Laptinskiy, Rosenholm Jessica M., Tatiana Dolenko
- Журнал: Advances in Intelligent Systems and Computing
- Том: 636
- Год издания: 2018
- Первая страница: 173
- Последняя страница: 179
- DOI: 10.1007/978-3-319-63940-6_24
- Аннотация: 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%.
- Добавил в систему: Лаптинский Кирилл Андреевич