ISSN 2353-6977 (Online)
The paper presents results of research on neural network application in forecasting the tensile strength of two types of sutures. The preliminary research was conducted in order to establish the accuracy of the proposed method and will be used for formulating further research areas. The neural network enabled evaluation of suture material degradation after 3-to-6-days’ exposure to Ringer’s solution. The encountered problems regarding inaccuracies show that developing a single model for sutures may be difficult or impossible. Therefore future research should be conducted for a single type of sutures only and require applying additional parameters for the neural network.
- APA 6th style
Karpiński, R., Gajewski, J., Szabelski, J., & Barta, D. (2017). Analysis of tensile strength of medium- and long-term absorbable sutures using neural networks. Applied Computer Science, 13(4), 76-86. doi:10.23743/acs-2017-31
- Chicago style
Karpiński, Robert, Jakub Gajewski, Jakub Szabelski, and Dalibor Barta. "Analysis of Tensile Strength of Medium- and Long-Term Absorbable Sutures Using Neural Networks." Applied Computer Science 13, no. 4 (2017): 76-86.
- IEEE style
R. Karpiński, J. Gajewski, J. Szabelski, and D. Barta, "Analysis of tensile strength of medium- and long-term absorbable sutures using neural networks," Applied Computer Science, vol. 13, no. 4, pp. 76-86, 2017.
- Vancouver style
Karpiński R, Gajewski J, Szabelski J, Barta D. Analysis of tensile strength of medium- and long-term absorbable sutures using neural networks. Applied Computer Science. 2017;13(4):76-86.
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