Gelişmiş Arama

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dc.contributor.authorSaray Çetinkaya, Tuğba
dc.contributor.authorSenan, Sibel
dc.contributor.authorOrman, Zeynep
dc.date.accessioned2023-10-18T17:54:06Z
dc.date.available2023-10-18T17:54:06Z
dc.date.issued2022en_US
dc.identifier.issn1062-7391
dc.identifier.issn1573-8736
dc.identifier.urihttps://hdl.handle.net/11363/5946
dc.description.abstractIn this study, a miner monitoring system is designed using the Deep Learning (DL) approach and the IoT technology together. It is aimed to determine the area where the miners are located while a possible accident occurs by the proposed system. Experiments were carried out to analyze the effectiveness of the proposed system and the performance evaluations were made. The best result was obtained with an accuracy rate of 97.14%. This rate indicates that the designed miner monitoring system can be used effectively in practice.en_US
dc.language.isoengen_US
dc.publisherPLEIADES PUBLISHING INC, PLEIADES HOUSE, 7 W 54 ST, NEW YORK, NY 10019, UNITED STATESen_US
dc.relation.isversionof10.1134/S1062739122020156en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectInternet of Things (IoT)en_US
dc.subjectminer monitoringen_US
dc.subjectartificial neural networksen_US
dc.subjectdeep learningen_US
dc.subjectLSTM modelen_US
dc.titleDeep Learning and Internet of Things (IoT) Based Monitoring System for Minersen_US
dc.typearticleen_US
dc.relation.ispartofJournal of Mining Scienceen_US
dc.departmentİstanbul Gelişim Meslek Yüksekokuluen_US
dc.identifier.volume58en_US
dc.identifier.issue2en_US
dc.identifier.startpage325en_US
dc.identifier.endpage337en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.institutionauthorSaray Çetinkaya, Tuğba


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