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Original scientific paper

THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES

Aneta MIRCESKA ; Technology, University Sts. Cyril and Methodius,Faculty of Electrical Engineering and Information, Skopje, Republic of Macedonia
Andrea KULAKOV ; Technology, University Sts. Cyril and Methodius,Faculty of Electrical Engineering and Information, Skopje, Republic of Macedonia
Saso STOLESKI ; Technology, University Sts. Cyril and Methodius,Faculty of Electrical Engineering and Information, Skopje, Republic of Macedonia

Fulltext: english, pdf (611 KB) pages 1-13 downloads: 493* cite
APA 6th Edition
MIRCESKA, A., KULAKOV, A. & STOLESKI, S. (2009). THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES. Engineering Review, 29 (2), 1-13. Retrieved from https://hrcak.srce.hr/45991
MLA 8th Edition
MIRCESKA, Aneta, et al. "THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES." Engineering Review, vol. 29, no. 2, 2009, pp. 1-13. https://hrcak.srce.hr/45991. Accessed 7 Jun. 2020.
Chicago 17th Edition
MIRCESKA, Aneta, Andrea KULAKOV and Saso STOLESKI. "THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES." Engineering Review 29, no. 2 (2009): 1-13. https://hrcak.srce.hr/45991
Harvard
MIRCESKA, A., KULAKOV, A., and STOLESKI, S. (2009). 'THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES', Engineering Review, 29(2), pp. 1-13. Available at: https://hrcak.srce.hr/45991 (Accessed 07 June 2020)
Vancouver
MIRCESKA A, KULAKOV A, STOLESKI S. THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES. Engineering Review [Internet]. 2009 [cited 2020 June 07];29(2):1-13. Available from: https://hrcak.srce.hr/45991
IEEE
A. MIRCESKA, A. KULAKOV and S. STOLESKI, "THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES", Engineering Review, vol.29, no. 2, pp. 1-13, 2009. [Online]. Available: https://hrcak.srce.hr/45991. [Accessed: 07 June 2020]
Fulltext: croatian, pdf (611 KB) pages 1-13 downloads: 473* cite
APA 6th Edition
MIRCESKA, A., KULAKOV, A. & STOLESKI, S. (2009). ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA. Engineering Review, 29 (2), 1-13. Retrieved from https://hrcak.srce.hr/45991
MLA 8th Edition
MIRCESKA, Aneta, et al. "ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA." Engineering Review, vol. 29, no. 2, 2009, pp. 1-13. https://hrcak.srce.hr/45991. Accessed 7 Jun. 2020.
Chicago 17th Edition
MIRCESKA, Aneta, Andrea KULAKOV and Saso STOLESKI. "ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA." Engineering Review 29, no. 2 (2009): 1-13. https://hrcak.srce.hr/45991
Harvard
MIRCESKA, A., KULAKOV, A., and STOLESKI, S. (2009). 'ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA', Engineering Review, 29(2), pp. 1-13. Available at: https://hrcak.srce.hr/45991 (Accessed 07 June 2020)
Vancouver
MIRCESKA A, KULAKOV A, STOLESKI S. ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA. Engineering Review [Internet]. 2009 [cited 2020 June 07];29(2):1-13. Available from: https://hrcak.srce.hr/45991
IEEE
A. MIRCESKA, A. KULAKOV and S. STOLESKI, "ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA", Engineering Review, vol.29, no. 2, pp. 1-13, 2009. [Online]. Available: https://hrcak.srce.hr/45991. [Accessed: 07 June 2020]

Abstracts
An artificial neural network is a system based on the operation of biological neural networks, in other words, it is an emulation of the biological neural system. The objective of this study is to compare the performance of two different versions of neural network ART algorithms such as Fuzzy ART vs. ARTFC methods used for classification of pulmonary function, detecting restrictive, obstructive and normal patterns of respiratory abnormalities by means of each of the neural networks, as well as the data gathered from spirometry. The spirometry data were obtained from 150 patients by standard acquisition protocol, 100 subjects used for training and 50 subjects for testing, respectively. The results showed that the standard Fuzzy ART grows faster than ARTFC, which successfully solves the category proliferation problem.

Keywords
Adaptive Resonance Theory; Art-Based Fuzzy Classifiers; Fuzzy Adaptive Resonance Theory

Hrčak ID: 45991

URI
https://hrcak.srce.hr/45991

[croatian]

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