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

dc.contributor.author Jafarian, A.
dc.contributor.author Baleanu, D.
dc.contributor.author Senel, M.
dc.contributor.author Okur, S.
dc.contributor.author Darwish, H.
dc.contributor.authorID 56389 tr_TR
dc.contributor.other 02.02. Matematik
dc.contributor.other 02. Fen-Edebiyat Fakültesi
dc.contributor.other 01. Çankaya Üniversitesi
dc.date.accessioned 2020-05-15T08:11:18Z
dc.date.accessioned 2025-09-18T14:10:49Z
dc.date.available 2020-05-15T08:11:18Z
dc.date.available 2025-09-18T14:10:49Z
dc.date.issued 2015
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. © 2015 American Scientific Publishers. en_US
dc.description.publishedMonth 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. en_US
dc.identifier.doi 10.1166/jctn.2015.4373
dc.identifier.issn 1546-1955
dc.identifier.scopus 2-s2.0-84983196381
dc.identifier.uri https://doi.org/10.1166/jctn.2015.4373
dc.identifier.uri https://hdl.handle.net/20.500.12416/13815
dc.language.iso en en_US
dc.publisher American Scientific Publishers en_US
dc.relation.ispartof Journal of Computational and Theoretical Nanoscience en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Adsorption And Desorption en_US
dc.subject Ann Analysis en_US
dc.subject Co<Sub>2</Sub> en_US
dc.subject Gas Sensors en_US
dc.subject Humidity en_US
dc.subject O<Sub>2</Sub> en_US
dc.subject Polypyrrole-Qcm en_US
dc.title Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis en_US
dc.title Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis tr_TR
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Baleanu, Dumitru
gdc.author.scopusid 56177297100
gdc.author.scopusid 25031262700
gdc.author.scopusid 7005872966
gdc.author.scopusid 18435088800
gdc.author.scopusid 55926032200
gdc.description.department Çankaya University en_US
gdc.description.departmenttemp Darwish H., Department of Physics, Faculty of Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Jafarian A., Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran; Baleanu D., Cankaya University, Faculty of Art and Sciences, Department of Mathematics and Computer Sciences, Balgat, Ankara, 06530, Turkey, Institute of Space Sciences, Magurele-Bucharest, P.O. Box, R 76900, Romania; Senel M., Department of Chemistry, Fatih University, Istanbul, Turkey; Okur S., Material Science and Engineering Department, Izmir Katip Çelebi University, Izmir, 35620, Turkey en_US
gdc.description.endpage 4398 en_US
gdc.description.issue 11 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.startpage 4392 en_US
gdc.description.volume 12 en_US
gdc.identifier.openalex W2791802792
gdc.openalex.fwci 0.0
gdc.openalex.normalizedpercentile 0.28
gdc.opencitations.count 2
gdc.plumx.mendeley 3
gdc.plumx.scopuscites 2
gdc.scopus.citedcount 2
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