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An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold
The definition and nature of information have perplexed scientists due to its dual nature in measurements. The information is discrete and continuous when evaluated on a metric scale, and the Laplace-Beltrami operator and Gauss-Bonnet Theorem can map one to another. On the other hand, defining the i...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10360576/ https://www.ncbi.nlm.nih.gov/pubmed/37484350 http://dx.doi.org/10.1016/j.heliyon.2023.e16653 |
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author | Koltuksuz, Ahmet Yucel, Cagatay Maazu Kademi, Anas |
author_facet | Koltuksuz, Ahmet Yucel, Cagatay Maazu Kademi, Anas |
author_sort | Koltuksuz, Ahmet |
collection | PubMed |
description | The definition and nature of information have perplexed scientists due to its dual nature in measurements. The information is discrete and continuous when evaluated on a metric scale, and the Laplace-Beltrami operator and Gauss-Bonnet Theorem can map one to another. On the other hand, defining the information as a discrete entity on the surface area of an n-dimensional discrete digital manifold provides a unique way of calculating the entropy of a manifold. The software simulation shows that the surface area of the discrete n-dimensional digital manifold is an effectively computable function. Moreover, it also provides the information-geometrical evaluation of Shannon information metrics. |
format | Online Article Text |
id | pubmed-10360576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103605762023-07-22 An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold Koltuksuz, Ahmet Yucel, Cagatay Maazu Kademi, Anas Heliyon Research Article The definition and nature of information have perplexed scientists due to its dual nature in measurements. The information is discrete and continuous when evaluated on a metric scale, and the Laplace-Beltrami operator and Gauss-Bonnet Theorem can map one to another. On the other hand, defining the information as a discrete entity on the surface area of an n-dimensional discrete digital manifold provides a unique way of calculating the entropy of a manifold. The software simulation shows that the surface area of the discrete n-dimensional digital manifold is an effectively computable function. Moreover, it also provides the information-geometrical evaluation of Shannon information metrics. Elsevier 2023-06-05 /pmc/articles/PMC10360576/ /pubmed/37484350 http://dx.doi.org/10.1016/j.heliyon.2023.e16653 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Koltuksuz, Ahmet Yucel, Cagatay Maazu Kademi, Anas An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title | An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title_full | An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title_fullStr | An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title_full_unstemmed | An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title_short | An information geometrical evaluation of Shannon information metrics on a discrete n-dimensional digital manifold |
title_sort | information geometrical evaluation of shannon information metrics on a discrete n-dimensional digital manifold |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10360576/ https://www.ncbi.nlm.nih.gov/pubmed/37484350 http://dx.doi.org/10.1016/j.heliyon.2023.e16653 |
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