DCA-IoMT: Knowledge-graph-embedding-enhanced deep collaborative alert recommendation against COVID-19
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Institute Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Filtration 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.
Açıklama
This 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.
Anahtar Kelimeler
COVID-19, Informatics, Adaptation Models, Semantics, Predictive Models, Network
Kaynak
IEEE Transactions On Industrial Informatics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
18
Sayı
12
Künye
Khan, 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.
