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Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis

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dc.contributor.author Darwish, Hamida
dc.contributor.author Jafarian, Ahmad
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Senel, Mehmet
dc.contributor.author Okur, Salih
dc.date.accessioned 2020-05-15T08:11:18Z
dc.date.available 2020-05-15T08:11:18Z
dc.date.issued 2015-11
dc.identifier.citation Darwish, H...et al. (2015). "Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis",Journal of Computational and Theoretical Nanoscience, Vol. 12, No. 11, pp. 4392-4398. tr_TR
dc.identifier.issn 15461955
dc.identifier.uri http://hdl.handle.net/20.500.12416/3804
dc.description.abstract In this study, the electrochemical deposition technique was used to fabricate Polyprrole thin film. The QCM piezoelectric sensors have been used to investigate the possible sensing mechanisms and adsorption-desorption kinetics of the polyprrole films to compare sensor sensitivities of the atmosferic gasses such as humidity, CO2 and O2. The Langmuir model and ANN Technique have been used to Polypyrrole Gas Sensor Data for environmental analysis. For this, feedback, three layer ANN has been used for the experimental data for adsorption and desorption process of PPY versus humidity, PPY versus CO2 and PPy versus O2. Different number of hidden layer used in this work and good result gets with 14 neurons. Totally 2064 experimental data used for fitting ANN. The randomly selected data was used to training and the ANN was terminated when the error was less than 10-3. tr_TR
dc.language.iso eng tr_TR
dc.publisher American Scientific Publishers tr_TR
dc.relation.isversionof 10.1166/jctn.2015.4373 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Adsorption and Desorption tr_TR
dc.subject CO2 tr_TR
dc.subject ANN Analysis tr_TR
dc.subject Humidity tr_TR
dc.subject Gas Sensors tr_TR
dc.subject Polypyrrole-QCM tr_TR
dc.subject O2 tr_TR
dc.title Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Computational and Theoretical Nanoscience tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 12 tr_TR
dc.identifier.issue 11 tr_TR
dc.identifier.startpage 4392 tr_TR
dc.identifier.endpage 4398 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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