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Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization.

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第一作者： Thirunavukarasu,Balasubramaniam

第一单位： School of Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia.;Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia.

作者： Thirunavukarasu,Balasubramaniam ^[1] ; David J,Warne ^[2] ; Richi,Nayak ^[1] ; Kerrie,Mengersen ^[2]

作者单位： School of Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia.;Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia. ^[1] Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia.;School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000 Australia. ^[2]

关键词 COVID-19 Clustering Explainable AI Nonnegative tensor factorization Pattern mining Stochastic epidemiological modelling

DOI 10.1007/s41060-022-00324-1

PMID 35528806

发布时间 2023-04-12

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International journal of data science and analytics

2023年15卷3期

267-280页

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International journal of data science and analytics

2023年15卷3期

267-280页

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Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization.

International journal of data science and analytics

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International journal of data science and analytics

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Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization.

International journal of data science and analytics

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International journal of data science and analytics

相关期刊

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