Anomaly Detection İn Walking Trajectory [yürüyüş Yörüngesinde Anormallik Algılama]

dc.contributor.authorÖğrenci, Arif Selçuk
dc.date.accessioned2019-06-28T11:11:19Z
dc.date.available2019-06-28T11:11:19Z
dc.date.issued2018
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAnalysis of the walking trajectory and the detection of anomalies in this trajectory provide important benefits in the fields of health and security. In this work two methods to detect anomalies in trajectories are compared. Firstly an unsupervised method is used where the conformance among trajectories are taken into consideration. Trajectories that deviate from others are qualified as anomalies. Secondly the points in the trajectories are considered as a time series. Artifical neural networks performing supervised learning based on the backpropagation algorithm are used. The results are compared and the points to be enhanced are highlighted.en_US]
dc.identifier.citation2
dc.identifier.doi10.1109/SIU.2018.8404797en_US
dc.identifier.endpage4
dc.identifier.isbn9781538615010
dc.identifier.scopus2-s2.0-85050824546en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1538
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404797
dc.institutionauthorÖğrenci, Arif Selçuken_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journal26th Signal Processing and Communications Applications Conference (SIU)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAnomaly detectionen_US
dc.subjectArtifical neural networksen_US
dc.subjectConformal predictionen_US
dc.subjectTrajectoryen_US
dc.titleAnomaly Detection İn Walking Trajectory [yürüyüş Yörüngesinde Anormallik Algılama]en_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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