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dc.contributor.authorErcan, Naci Mert
dc.contributor.authorSert, Mustafa
dc.date.accessioned2022-06-13T13:43:49Z
dc.date.available2022-06-13T13:43:49Z
dc.date.issued2021
dc.identifier.isbn978-1-6654-3734-9en_US
dc.identifier.urihttp://hdl.handle.net/11727/7006
dc.description.abstractThe use of smart devices in home environments has been increasing in recent years. The wireless connection of these devices to the internet enables smart homes to be built with less cost and hence, recognition of activities in home environments and the detection of possible anomalies in activities is important for several applications. In this study, we propose a new method based on the changepoint representation of sensor data and variable-length windowing for the recognition of abnormal activities. We present comparative analyses with different representations to demonstrate the efficacy of the proposed scheme. Our results on the WSU performance dataset show that, the use of variable-length windowing improves the anomaly detection performance in comparison to fixed-length windowing.en_US
dc.description.sponsorshipIEEE; IEEE Comp Socen_US
dc.language.isoengen_US
dc.relation.isversionof10.1109/ISM52913.2021.00012en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectsmart home sensorsen_US
dc.subjectanomaly detectionen_US
dc.titleAnomaly Detection in Smart Home Environments using Convolutional Neural Networken_US
dc.typeProceedings Paperen_US
dc.relation.journal23RD IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2021)en_US
dc.identifier.startpage27en_US
dc.identifier.endpage30en_US
dc.identifier.wos000794252400005en_US
dc.identifier.scopus2-s2.0-85125019713en_US


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