Publication:
5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model

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dc.citedby1
dc.contributor.authorSamidi F.S.en_US
dc.contributor.authorMohamed Radzi N.A.en_US
dc.contributor.authorMohd Azmi K.H.en_US
dc.contributor.authorMohd Aripin N.en_US
dc.contributor.authorAzhar N.A.en_US
dc.contributor.authorid57215054855en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid57982272200en_US
dc.contributor.authorid57858108600en_US
dc.contributor.authorid57219033091en_US
dc.date.accessioned2023-05-29T09:36:49Z
dc.date.available2023-05-29T09:36:49Z
dc.date.issued2022
dc.description.abstractResource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model�s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical. � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8271
dc.identifier.doi10.3390/app12168271
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85136560848
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136560848&doi=10.3390%2fapp12168271&partnerID=40&md5=0923fb4c8bd99dd7f49822879e8b6600
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26802
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.title5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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