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Cited 2 time in webofscience Cited 5 time in scopus
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Design of tensorflow-based proactive smart home managers

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dc.contributor.authorPark, M.-H.-
dc.contributor.authorJang, Y.-H.-
dc.contributor.authorJu, Y.-W.-
dc.contributor.authorPark, S.-C.-
dc.date.available2020-02-27T12:42:36Z-
dc.date.created2020-02-12-
dc.date.issued2018-
dc.identifier.issn1876-1100-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4279-
dc.description.abstractIn recent years, with IoT(Internet of Things) technology as the main focus, device operation and control technology in smart homes has been attracting considerable attention, and home IoT device management services are being provided by various companies, including communication companies. The smart home manager system manages smart devices used in homes, and it provides only the status value information and control function of the currently registered devices. Thus, unnecessary access procedures occur due to the characteristic of the smart home, which uses a smart device repeatedly for the same purpose. To resolve such shortcomings, in this paper, the Proactive Smart Home Manager has been designed, which can predict and suggest users the next steps to take by user usage pattern analysis and inference via machine learning. © Springer Nature Singapore Pte Ltd. 2018.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.relation.isPartOfLecture Notes in Electrical Engineering-
dc.subjectArtificial intelligence-
dc.subjectAutomation-
dc.subjectIntelligent buildings-
dc.subjectLearning systems-
dc.subjectManagers-
dc.subjectUbiquitous computing-
dc.subjectCommunication companies-
dc.subjectControl functions-
dc.subjectDevice management-
dc.subjectDevice operations-
dc.subjectSmart devices-
dc.subjectSmart homes-
dc.subjectTensorFlow-
dc.subjectUsage patterns-
dc.subjectInternet of things-
dc.titleDesign of tensorflow-based proactive smart home managers-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.wosid000437317300014-
dc.identifier.doi10.1007/978-981-10-7605-3_14-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, v.474, pp.83 - 89-
dc.identifier.scopusid2-s2.0-85039439317-
dc.citation.endPage89-
dc.citation.startPage83-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.volume474-
dc.contributor.affiliatedAuthorPark, M.-H.-
dc.contributor.affiliatedAuthorJang, Y.-H.-
dc.contributor.affiliatedAuthorPark, S.-C.-
dc.type.docTypeProceedings Paper-
dc.subject.keywordAuthorIoT-
dc.subject.keywordAuthorLogistic Classification-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorSmart home manager-
dc.subject.keywordAuthorTensorFlow-
dc.subject.keywordPlusArtificial intelligence-
dc.subject.keywordPlusAutomation-
dc.subject.keywordPlusIntelligent buildings-
dc.subject.keywordPlusLearning systems-
dc.subject.keywordPlusManagers-
dc.subject.keywordPlusUbiquitous computing-
dc.subject.keywordPlusCommunication companies-
dc.subject.keywordPlusControl functions-
dc.subject.keywordPlusDevice management-
dc.subject.keywordPlusDevice operations-
dc.subject.keywordPlusSmart devices-
dc.subject.keywordPlusSmart homes-
dc.subject.keywordPlusTensorFlow-
dc.subject.keywordPlusUsage patterns-
dc.subject.keywordPlusInternet of things-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.description.journalRegisteredClassscopus-
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