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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBelhadi, Asma
dc.contributor.authorSrivastava, Gautam
dc.contributor.authorLin, Jerry Chun-Wei
dc.date.accessioned2022-05-06T08:56:14Z
dc.date.available2022-05-06T08:56:14Z
dc.date.created2022-01-04T14:18:31Z
dc.date.issued2021
dc.identifier.citationDjenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C.-W. (2021). Secure collaborative augmented reality framework for biomedical informatics. IEEE Journal of Biomedical and Health Informatics, 1-1.en_US
dc.identifier.issn2168-2194
dc.identifier.urihttps://hdl.handle.net/11250/2994506
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen_US
dc.description.abstractAugmented reality is currently a great interest in biomedical health informatics. At the same time, several challenges have been appeared, in particular with the rapid progress of smart sensors technologies, and medical artificial intelligence. This yields the necessity of new needs in biomedical health informatics. Collaborative learning and privacy are some of the challenges of augmented reality technology in biomedical health informatics. This paper introduces a novel secure collaborative augmented reality framework for biomedical health informatics-based applications. Distributed deep learning is first performed across a multi-agent system platform. The privacy strategy is developed for ensuring better communications of the different intelligent agents in the system. In this research work, a system of multiple agents is created for the simulation of the collective behaviours of the smart components of biomedical health informatics. Augmented reality is also incorporated for better visualization of the resulted medical patterns. A novel privacy strategy based on blockchain is investigated for ensuring the confidentiality of the learning process. Experiments are conducted on the real use case of the biomedical segmentation process. Our strong experimental analysis reveals the strength of the proposed framework when directly compared to state-of-the-art biomedical health informatics solutions.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.subjectbiomedical health informaticsen_US
dc.subjectaugmented realityen_US
dc.subjectdistributed deep learningen_US
dc.subjectmulti-agent systemen_US
dc.subjectprivacyen_US
dc.titleSecure Collaborative Augmented Reality Framework for Biomedical Informaticsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2021 IEEEen_US
dc.source.pagenumber8en_US
dc.source.journalIEEE journal of biomedical and health informaticsen_US
dc.identifier.doi10.1109/JBHI.2021.3139575
dc.identifier.cristin1974452
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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