Research Article | Open Access | Download Full Text
Volume 2 | Issue 3 | Year 2023 | Article Id: DST-V2I3P101 DOI: https://doi.org/10.59232/DST-V2I3P101
Persistent Homology and Artificial Intelligence Analysis of COVID-19 in Topological Spaces
Benard Okelo, Allan Onyango
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 30 Jun 2023 | 15 Jul 2023 | 02 Aug 2023 | 19 Aug 2023 |
Citation
Benard Okelo, Allan Onyango. “Persistent Homology and Artificial Intelligence Analysis of COVID-19 in Topological Spaces.” DS Journal of Digital Science and Technology, vol. 2, no. 3, pp. 1-8, 2023.
Abstract
In this study, we describe aspects of Topological Data Analysis using techniques like Persistent Homology and Simplicial Complex. We then explore Big Data Sets of COVID-19 with extremely large and highly complex properties. We, after that, discuss the characterization and significance of the Hausdorff Spaces an AI in this work. Next, we describe simulations within the Python programming language with applications in TDA.
Keywords
Artificial Intelligence, Machine Learning, Topological Data Analysis, COVID-19, Python.
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