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      Data Quality and the Internet of Medical Things (IoMT)

      Published
      proceedings-article
      ,  
      35th International BCS Human-Computer Interaction Conference (HCI2022)
      Towards a Human-Centred Digital Society
      July 11th to 13th, 2022
      Internet of Medical Things (IoMT), Data Quality, Federated Learning (FL)
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            Abstract

            Content

            Author and article information

            Contributors
            Conference
            July 2022
            July 2022
            : 1-2
            Affiliations
            [0001]School of Computing and Mathematics

            Keele University, UK
            Article
            10.14236/ewic/HCI2022.65
            1290df52-16a1-4a88-b608-5efac1659dfa
            © Farhad et al. Published by BCS Learning & Development. Proceedings of the 35th British HCI and Doctoral Consortium 2022, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            35th International BCS Human-Computer Interaction Conference
            HCI2022
            35
            Keele, Staffordshire
            July 11th to 13th, 2022
            Electronic Workshops in Computing (eWiC)
            Towards a Human-Centred Digital Society
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.65
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Federated Learning (FL),Data Quality,Internet of Medical Things (IoMT)

            REFERENCES

            1. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58

            2. Chen, K.-C., & Lien, S.-Y. (2014). Machine-to-machine communications: Technologies and challenges. Ad Hoc Networks, 18, 3-23

            3. Farhad, A, Woolley, S.I, Andras, A, “A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things”, Medical Informatics Europe Conference (MIE), In Public Health and Informatics (pp. 504-505). IOS Press.2021.

            4. Farhad, A., Woolley, S.I., and Andras, P. “Federated learning for AI to improve patient care using wearable and IoMT sensors”, In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 434-434, IEEE.

            5. Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016a). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57-81.

            6. Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security: Challenges and 14 Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 11, 100227.

            7. Perez-Castillo, R., Carretero, A. G., Rodriguez, M., Caballero, I., & Piattini, M. (2018). Data Quality Best Practices in IoT Environments. 2018 International Conference on the Quality of Information and Communications Technology, 272-275

            8. Ray, D., Collins, T., Woolley, S. and Ponnapalli, P., 2021. A Review of Wearable Multi-wavelength Photoplethysmography. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2021.3121476.

            9. Wei, X., Liu, Y., Wang, X., Sun, B., Gao, S., & Rokne, J. (2019a). A survey on quality-assurance approximate stream processing and applications. Future Generation Computer Systems, 1062-1080.

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