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Bio-Algorithms and Med-Systems

Editor-in-Chief: Roterman-Konieczna , Irena


CiteScore 2018: 0.29

SCImago Journal Rank (SJR) 2018: 0.129
Source Normalized Impact per Paper (SNIP) 2018: 0.324

ICV 2018: 120.80

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1896-530X
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Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration

Martyna Sasiada
  • Department of Control and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Aneta Fraczek-Szczypta
  • Department of Biomaterials, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ryszard Tadeusiewicz
  • Corresponding author
  • Department of Control and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-02-28 | DOI: https://doi.org/10.1515/bams-2016-0025

Abstract

A new method of predicting the properties of carbon nanomaterials from carbon nanotubes and graphene oxide, using electrophoretic deposition (EPD) on a metal surface, was investigated. The main goal is to obtain the basis for nervous tissue stimulation and regeneration. Because of the many variations of the EPD method, costly and time-consuming experiments are necessary for optimization of the produced systems. To limit such costs and workload, we propose a neural network-based model that can predict the properties of selected carbon nanomaterial systems before they are produced. The choice of neural networks as predictive learning models is based on many studies in the literature that report neural models as good interpretations of real-life processes. The use of a neural network model can reduce experimentation with unpromising methods of systems processing and preparation. Instead, it allows a focus on experiments with these systems, which are promising according to the prediction given by the neural model. The performed tests showed that the proposed method of predictive learning of carbon nanomaterial properties is easy and effective. The experiments showed that the prediction results were consistent with those obtained in the real system.

Keywords: artificial neural networks; carbon nanotubes; graphene; nanomaterials; nervous tissue regeneration; stimulation

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About the article

Received: 2016-12-01

Accepted: 2017-01-10

Published Online: 2017-02-28

Published in Print: 2017-03-01


Author contributions: The authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This work was supported by the National Science Centre – Poland (NCN; grant no. UMO-2013/11/D/ST8/03272), “Carbon nanomaterial coatings on the metal surface as a potential systems for nerve cell regeneration and stimulation”, financed from NCN resources.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


Citation Information: Bio-Algorithms and Med-Systems, Volume 13, Issue 1, Pages 25–35, ISSN (Online) 1896-530X, ISSN (Print) 1895-9091, DOI: https://doi.org/10.1515/bams-2016-0025.

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