DCA-IoMT: Knowledge-graph-embedding-enhanced deep collaborative alert recommendation against COVID-19

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dc.authorid0000-0002-4942-9583en_US
dc.authorid0000-0001-7780-6473en_US
dc.authorid0000-0002-3999-4917en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorKhan, Nasrullah
dc.contributor.authorMa, Zongmin
dc.contributor.authorUllah, Aman
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-09-06T08:13:09Z
dc.date.available2023-09-06T08:13:09Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported in part by the Basic Research Program of Jiangsu Province and in part by the National Natural Science Foundation of China under Grant BK20191274, Grant 62176121, and Grant 61772269.en_US
dc.description.abstractFiltration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people-an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.en_US
dc.description.sponsorshipBasic Research Program of Jiangsu Province; National Natural Science Foundation of China [BK20191274, 62176121, 61772269]en_US
dc.identifier.citationKhan, N., Ma, Z., Ullah, A., & Polat, K. (2022). DCA-IoMT: Knowledge-Graph-Embedding-Enhanced Deep Collaborative Alert Recommendation Against COVID-19. IEEE Transactions on Industrial Informatics, 18(12), 8924-8935.en_US
dc.identifier.doi10.1109/TII.2022.3159710
dc.identifier.endpage8935en_US
dc.identifier.issn1551-3203
dc.identifier.issn1941-0050
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85127030482en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage8924en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TII.2022.3159710
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11665
dc.identifier.volume18en_US
dc.identifier.wosWOS:000862429800057en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherIEEE-Institute Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Industrial Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectInformaticsen_US
dc.subjectAdaptation Modelsen_US
dc.subjectSemanticsen_US
dc.subjectPredictive Modelsen_US
dc.subjectNetworken_US
dc.titleDCA-IoMT: Knowledge-graph-embedding-enhanced deep collaborative alert recommendation against COVID-19en_US
dc.typeArticleen_US

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