Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12715
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dc.contributor.authorPrathanrat P.
dc.contributor.authorPolprasert C.
dc.date.accessioned2021-04-05T03:05:12Z-
dc.date.available2021-04-05T03:05:12Z-
dc.date.issued2018
dc.identifier.other2-s2.0-85060037072
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/12715-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060037072&doi=10.1109%2fICIIBMS.2018.8550030&partnerID=40&md5=8fdf63797476d9d48b4b3d813543a032
dc.description.abstractIn this paper, we employ machine learning to predict the performance of Jupyter notebook on JupyterHub. We show that the notebook's CPU profile, the notebook's RAM profile, number of users and average delay between cells are crucial features that impact the performance of the machine learning models to accurately predict the performance of Jupyter notebook in term of the response time. We characterize the performance of our model to predict the notebook's response time in terms of the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results show that the random forest model yields strongest performance to predict the performance of Jupyter notebook with MAPE equal to 9.849% and MAE equal to 13.768 seconds. with r-square equal to 0.93. © 2018 IEEE.
dc.subjectArtificial intelligence
dc.subjectDecision trees
dc.subjectForecasting
dc.subjectAverage delay
dc.subjectJupyter Notebook
dc.subjectJupyterHub
dc.subjectMachine learning models
dc.subjectMean absolute error
dc.subjectMean absolute percentage error
dc.subjectPerformance prediction
dc.subjectRandom forest modeling
dc.subjectLearning systems
dc.titlePerformance Prediction of Jupyter Notebook in JupyterHub using Machine Learning
dc.typeConference Paper
dc.rights.holderScopus
dc.identifier.bibliograpycitation2018 International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2018. Vol , No. (2018), p.157-162
dc.identifier.doi10.1109/ICIIBMS.2018.8550030
Appears in Collections:Scopus 1983-2021

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