Web of Science:
Classification of Ionospheric Disturbances Using Long Short Term Memory Algorithm

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dc.contributor.authorGul, S.E.
dc.contributor.authorKaratay, S.
dc.contributor.authorArikan, F.
dc.date.accessioned2024-12-07T16:12:24Z
dc.date.available2024-12-07T16:12:24Z
dc.date.issued2024.01.01
dc.description.abstractIn this study, disturbances in the ionosphere during periods of geomagnetic activity and seismic activity are classified with the Long Short Term Memory algorithm, one of the Deep Learning algorithms. It is observed that the classification Accuracy is at least 84% in the classification of five earthquake and five disturbance days based on the Total Electron Content data input.
dc.identifier.doi10.1109/SIU61531.2024.10600803
dc.identifier.endpage
dc.identifier.issn2165-0608
dc.identifier.issue
dc.identifier.startpage
dc.identifier.urihttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001297894700072&DestLinkType=FullRecord&DestApp=WOS_CPL
dc.identifier.urihttps://hdl.handle.net/20.500.12597/33846
dc.identifier.volume
dc.identifier.wos001297894700072
dc.language.isotr
dc.relation.ispartof32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectLong Short Term Memory
dc.subjectionospheric disturbances
dc.subjectTotal Electron Content
dc.titleClassification of Ionospheric Disturbances Using Long Short Term Memory Algorithm
dc.typeOther
dspace.entity.typeWos

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