Publication:
Classification of Hospital of the Future Applications using Machine Learning

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dc.citedby0
dc.contributor.authorZulkifli I.T.en_US
dc.contributor.authorRadzi N.A.M.en_US
dc.contributor.authorAripin N.M.en_US
dc.contributor.authorAzmi K.H.M.en_US
dc.contributor.authorSamidi F.S.en_US
dc.contributor.authorAzhar N.A.en_US
dc.contributor.authorid57982505900en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid35092180800en_US
dc.contributor.authorid57982272200en_US
dc.contributor.authorid57215054855en_US
dc.contributor.authorid57219033091en_US
dc.date.accessioned2024-10-14T03:20:36Z
dc.date.available2024-10-14T03:20:36Z
dc.date.issued2023
dc.description.abstractEffective health management is critical to ensure patients have access to necessary healthcare services. There are a number of challenges that can limit the provision of medical treatment, including a shortage of healthcare professionals, limited resources, and geographical barriers. Hospital of the Future (HoF) incorporates a number of technologies and innovations to improve the delivery of healthcare services and support effective health management. 5G network slicing has the potential to greatly enhance the capabilities of hospitals and the delivery of healthcare services. The network can be sliced into three main servicesen_US
dc.description.abstracteMBB, mMTC, and URLLC. This paper presented a comparison of various supervised machine learning models in predicting the three network services. The classification for the slices is based on HoF applications' requirements. Deep learning model has the highest accuracy of 100% with total runtime of 85.7s and lowest standard deviation value. In comparison with other machine learning models, deep learning is the best model in predicting 5GHoF slices. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ISCAIE57739.2023.10165466
dc.identifier.epage17
dc.identifier.scopus2-s2.0-85165172183
dc.identifier.spage13
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85165172183&doi=10.1109%2fISCAIE57739.2023.10165466&partnerID=40&md5=97c3301aea6094e345d669dd99224bbf
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34551
dc.pagecount4
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023
dc.subject5G
dc.subjectDeep Learning
dc.subjectHospital of the Future
dc.subjectMachine Learning
dc.subjectNetwork slicing
dc.subject5G mobile communication systems
dc.subjectDeep learning
dc.subjectLearning systems
dc.subjectSupervised learning
dc.subject5g
dc.subjectDeep learning
dc.subjectFuture applications
dc.subjectHealth management
dc.subjectHealthcare services
dc.subjectHospital of the future
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectManagement IS
dc.subjectNetwork slicing
dc.subjectHospitals
dc.titleClassification of Hospital of the Future Applications using Machine Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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