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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/54505
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dc.contributor.authorNamazi, Samiraen
dc.contributor.authorBrankovic, Ljiljanaen
dc.contributor.authorMoghtaderi, Behdaden
dc.contributor.authorZanganeh, Jafaren
local.source.editorEditor(s): Bhuvan Unhelker, Hari Mohan Pandey and Gaurav Rajen
dc.date.accessioned2023-04-04T01:42:54Z-
dc.date.available2023-04-04T01:42:54Z-
dc.date.issued2022-
dc.identifier.citationApplications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2021, p. 529-543en
dc.identifier.isbn9789811948312en
dc.identifier.isbn9789811948305en
dc.identifier.isbn9789811948336en
dc.identifier.isbn9811948313en
dc.identifier.urihttps://hdl.handle.net/1959.11/54505-
dc.description.abstract<p>In order to better understand conditions that lead to methane explosions in underground coal mines, we apply machine learning to data collected in an industrial scale research project carried out at the University of Newcastle, Australia, 2014-2018 (VAM Abatement Safety Project). We present a comparison of five different methods (Decision Tree, Random Forest, Naïve Bayes, AdaBoostM1, and SVM with SMO) to classify the maximum pressure and maximum flame velocity in order to predict detonation and inform the design of capture ducts. All methods are evaluated with a tenfold cross validation technique. We found that tree-based classification methods provide the most accurate prediction of dangerous pressure and supersonic velocity.</p>en
dc.languageenen
dc.publisherSpringeren
dc.relation.ispartofApplications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2021en
dc.relation.ispartofseriesLecture Notes in Electrical Engineeringen
dc.relation.isversionof1en
dc.relation.urihttps://www.youtube.com/live/Cw6Ag-_ofFk?feature=shareen
dc.titlePredicting Deflagration and Detonation in Detonation Tubeen
dc.typeConference Publicationen
dc.relation.conferenceICAAAIML 2021: International Conference on Advances and Applications of Artificial Intelligence and Machine Learningen
dc.identifier.doi10.1007/978-981-19-4831-2_43en
local.contributor.firstnameSamiraen
local.contributor.firstnameLjiljanaen
local.contributor.firstnameBehdaden
local.contributor.firstnameJafaren
local.profile.schoolSchool of Science and Technologyen
local.profile.emaillbrankov@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference29th - 30th October, 2021en
local.conference.placeNoida, Indiaen
local.publisher.placeSingaporeen
local.format.startpage529en
local.format.endpage543en
local.series.issn1876-1119en
local.series.issn1876-1100en
local.series.number925en
local.peerreviewedYesen
local.contributor.lastnameNamazien
local.contributor.lastnameBrankovicen
local.contributor.lastnameMoghtaderien
local.contributor.lastnameZanganehen
dc.identifier.staffune-id:lbrankoven
local.profile.orcid0000-0002-5056-4627en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/54505en
local.date.onlineversion2022-09-14-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitlePredicting Deflagration and Detonation in Detonation Tubeen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsICAAAIML 2021: International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, Noida, India, 29th - 30th October, 2021en
local.search.authorNamazi, Samiraen
local.search.authorBrankovic, Ljiljanaen
local.search.authorMoghtaderi, Behdaden
local.search.authorZanganeh, Jafaren
local.uneassociationNoen
local.atsiresearchNoen
local.conference.venueSharda Universityen
local.sensitive.culturalNoen
local.year.available2022en
local.year.published2022en
local.subject.for2020480204 Mining, energy and natural resources lawen
local.subject.seo2020170601 Coal mining and extractionen
local.date.start2021-10-29-
local.date.end2021-10-30-
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypePre-UNEen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.relation.worldcathttps://www.worldcat.org/title/1344541041en
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