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Active Learning—Validation

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Active Learning to Minimize the Possible Risk of Future Epidemics

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

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Abstract

This chapter provides an overview of the datasets utilized, outlines the employed evaluation methods encompassing classification metrics and clustering indices, and elucidates the validation technique implemented.

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Notes

  1. 1.

    https://github.com/agchung/Figure1-COVID-chestxray-dataset.

  2. 2.

    https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.

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Authors and Affiliations

  1. Applied AI Research Lab, Department of Computer Science, University of South Dakota, Vermillion, USA

    KC Santosh

  2. Applied AI Research Lab, Department of Computer Science, University of South Dakota, Vermillion, USA

    Suprim Nakarmi

Authors
  1. KC Santosh
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  2. Suprim Nakarmi
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Santosh, K., Nakarmi, S. (2023). Active Learning—Validation. In: Active Learning to Minimize the Possible Risk of Future Epidemics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-99-7442-9_5

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