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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

Puni tekst: engleski, pdf (611 KB) str. 1-13 preuzimanja: 492* citiraj
APA 6th Edition
MIRCESKA, A., KULAKOV, A. i STOLESKI, S. (2009). THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES. Engineering Review, 29 (2), 1-13. Preuzeto s 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, br. 2, 2009, str. 1-13. https://hrcak.srce.hr/45991. Citirano 09.04.2020.
Chicago 17th Edition
MIRCESKA, Aneta, Andrea KULAKOV i Saso STOLESKI. "THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES." Engineering Review 29, br. 2 (2009): 1-13. https://hrcak.srce.hr/45991
Harvard
MIRCESKA, A., KULAKOV, A., i STOLESKI, S. (2009). 'THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES', Engineering Review, 29(2), str. 1-13. Preuzeto s: https://hrcak.srce.hr/45991 (Datum pristupa: 09.04.2020.)
Vancouver
MIRCESKA A, KULAKOV A, STOLESKI S. THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES. Engineering Review [Internet]. 2009 [pristupljeno 09.04.2020.];29(2):1-13. Dostupno na: https://hrcak.srce.hr/45991
IEEE
A. MIRCESKA, A. KULAKOV i S. STOLESKI, "THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES", Engineering Review, vol.29, br. 2, str. 1-13, 2009. [Online]. Dostupno na: https://hrcak.srce.hr/45991. [Citirano: 09.04.2020.]
Puni tekst: hrvatski, pdf (611 KB) str. 1-13 preuzimanja: 471* citiraj
APA 6th Edition
MIRCESKA, A., KULAKOV, A. i STOLESKI, S. (2009). ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA. Engineering Review, 29 (2), 1-13. Preuzeto s 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, br. 2, 2009, str. 1-13. https://hrcak.srce.hr/45991. Citirano 09.04.2020.
Chicago 17th Edition
MIRCESKA, Aneta, Andrea KULAKOV i Saso STOLESKI. "ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA." Engineering Review 29, br. 2 (2009): 1-13. https://hrcak.srce.hr/45991
Harvard
MIRCESKA, A., KULAKOV, A., i STOLESKI, S. (2009). 'ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA', Engineering Review, 29(2), str. 1-13. Preuzeto s: https://hrcak.srce.hr/45991 (Datum pristupa: 09.04.2020.)
Vancouver
MIRCESKA A, KULAKOV A, STOLESKI S. ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA. Engineering Review [Internet]. 2009 [pristupljeno 09.04.2020.];29(2):1-13. Dostupno na: https://hrcak.srce.hr/45991
IEEE
A. MIRCESKA, A. KULAKOV i S. STOLESKI, "ULOGA UMJETNE NEURONSKE MREŽE U DETEKCIJI ABNORMALNOSTI U FUNKCIJI RADA PLUĆA", Engineering Review, vol.29, br. 2, str. 1-13, 2009. [Online]. Dostupno na: https://hrcak.srce.hr/45991. [Citirano: 09.04.2020.]

Sažetak
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.

Ključne riječi
Adaptive Resonance Theory; Art-Based Fuzzy Classifiers; Fuzzy Adaptive Resonance Theory

Hrčak ID: 45991

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

[hrvatski]

Posjeta: 1.375 *