Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/24052
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dc.contributor.authorMircheska, Anetaen_US
dc.contributor.authorKulakov, Andreaen_US
dc.contributor.authorStoleski, Sashoen_US
dc.date.accessioned2022-11-01T12:49:33Z-
dc.date.available2022-11-01T12:49:33Z-
dc.date.issued2009-12-24-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/24052-
dc.description.abstractAn 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.en_US
dc.publisherFaculty of Engineering/Faculty of Civil Engineering, University of Rijekaen_US
dc.relation.ispartofEngineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstvaen_US
dc.subject- Adaptive Resonance Theory - Art-Based Fuzzy Classifiers - Fuzzy Adaptive Resonance Theoryen_US
dc.titleUloga umjetne neuronske mreže u detekciji abnormalnosti u funkciji rada plućaen_US
dc.typeArticleen_US
item.fulltextWith Fulltext-
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crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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