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A hybrid fingerprint based indoor positioning with extreme learning machine

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dc.contributor.authorGünal, Serkan
dc.contributor.authorAhmet Yazıcı
dc.contributor.authorSerkan Günal
dc.contributor.orcid0000-0002-8013-6922
dc.contributor.orcid0000-0001-5589-2032
dc.contributor.orcid0000-0002-9691-1575
dc.date.accessioned2025-11-13T10:54:00Z
dc.date.issued2017-05-01
dc.identifier.doihttps://doi.org/10.1109/siu.2017.7960161
dc.identifier.openalexW2729580475
dc.identifier.urihttps://hdl.handle.net/11421/5599
dc.identifier.urihttps://doi.org/10.1109/siu.2017.7960161
dc.language.isoen
dc.rightsrestrictedAccess
dc.subjectCluster analysis
dc.subjectComputer science
dc.subjectIndoor positioning system
dc.subjectFingerprint (computing)
dc.subjectExtreme learning machine
dc.subjectFeature selection
dc.subjectField (mathematics)
dc.subjectArtificial intelligence
dc.subjectHybrid positioning system
dc.subjectPositioning system
dc.subjectComputation
dc.subjectFeature (linguistics)
dc.subjectReal-time computing
dc.subjectMachine learning
dc.subjectData mining
dc.subjectEngineering
dc.subjectAlgorithm
dc.subjectAccelerometer
dc.subject.sdg9
dc.titleA hybrid fingerprint based indoor positioning with extreme learning machine
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5003391938

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