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[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
[[Category:Publications in 2021 year|{{PAGENAME}}]]
{{issue/title
| title = Intuitionistic fuzzy evaluation of artificial neural network model
| shortcut = nifs/27/4/71-77
}}
{{issue/author
| author = Todor Petkov
| institution = “Prof. Dr. Assen Zlatarov” University
| address = 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
| email-before-at = todor_petkov
| email-after-at = btu.bg
}}
{{issue/author
| author = Veselina Bureva
| institution = “Prof. Dr. Assen Zlatarov” University
| address = 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
| email-before-at = vbureva
| email-after-at = btu.bg
}}
{{issue/author
| author = Stanislav Popov
| institution = “Prof. Dr. Assen Zlatarov” University
| address = 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
| email-before-at = stani_popov
| email-after-at = yahoo.com
}}
{{issue/data
| issue = [[Notes on Intuitionistic Fuzzy Sets/27/4|Notes on Intuitionistic Fuzzy Sets, Volume 27 (2021), Number 4]], pages 71-77
| doi = https://doi.org/10.7546/nifs.2021.27.4.71-77
| file = NIFS-27-4-71-77.pdf
| format = PDF
| size = 1251
| abstract = In this paper a method that evaluates a trained artificial neural network is presented. The learning type of an artificial neural network is supervised learning which requires labeled input training vectors. Labeled medical data is provided to train the network, where the labels can either be 1 if the person is alive, or 0 if the person has deceased. The data is divided into training and validation vectors. The validation input vectors are used to evaluate the model and the results are summarized by using intuitionistic fuzzy values.
| keywords = Intuitionistic fuzzy evaluation, Intuitionistic fuzzy sets, Neural network.
| ams = 03E72.
| references =
# Aggarwal, C. (2018). Neural Networks and Deep Learning: A Textbook. 1st edition, Springer.
# Alama, A., Arana-Daniel, N., & Lopez-Franco, C. (2019). Artificial Neural Network for Engineering Applications. 1st Edition, Elsevier Inc.
# Atanassov, K. (2012). [[On Intuitionistic Fuzzy Sets Theory]]. Springer, Berlin.
# Atanassov, K. (2015). Intuitionistic fuzzy logics as tools for evaluation of Data Mining processes. Knowledge-Based Systems, 80, 122–130.
# Atanassov, K. (2020). Circular Intuitionistic Fuzzy Sets. Journal of Intelligent and Fuzzy Systems, 39(5), 5981–5986.
# Atanassov, K. (2020). [[Generalized Nets and Intuitionistic Fuzziness in Data Mining]]. “Prof. Marin Drinov” Publishing House of the Bulgarian Academy of Sciences, Sofia.
# Atanassov, K. (2020). Interval-Valued Intuitionistic Fuzzy Sets. Springer, Cham.
# Atanassov, K., & Marinov, E. (2021). Four Distances for Circular Intuitionistic Fuzzy Sets. Mathematics, 9(10), Article No. 1121.
# Atanassov, K., Szmidt, E., & Kacprzyk, J. (2013). [[Issue:On intuitionistic fuzzy pairs|On intuitionistic fuzzy pairs]]. Notes on Intuitionistic Fuzzy Sets, 19(3), 1–13.
# Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20, Article No. 16.
# Hagan, M., Demuth, H., Beale, M., & De Jesús, O. (2014). Neural Network Design. 2nd Edition, Retrieved from: https://hagan.okstate.edu/NNDesign.pdf. Accessed: 27.09.2021.
# Haley, W. (2018). Artificial Intelligence. Grey House Publishing.
# Sakhiya, Nayan (2021). Python: Heart Fail: Analysis and Quick-prediction, Retrieved from: https://www.kaggle.com/nayansakhiya/heart-fail-analysis-and-quick-prediction/data. Accessed: 27.09.2021.

| citations =
| see-also =
}}

Issue:Intuitionistic fuzzy evaluation of artificial neural network model - Revision history

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