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Predicting new crescent moon visibility applying machine learning algorithms

The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied...

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Autores principales: Al-Rajab, Murad, Loucif, Samia, Al Risheh, Yazan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126129/
https://www.ncbi.nlm.nih.gov/pubmed/37095098
http://dx.doi.org/10.1038/s41598-023-32807-x
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author Al-Rajab, Murad
Loucif, Samia
Al Risheh, Yazan
author_facet Al-Rajab, Murad
Loucif, Samia
Al Risheh, Yazan
author_sort Al-Rajab, Murad
collection PubMed
description The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of Ramadan month within the Muslim community. This is mainly due to the imprecise observations of the new crescent Moon in different locations. Artificial intelligence and its sub-field machine learning have shown great success in their application in several fields. In this paper, we propose the use of machine learning algorithms to help in determining the start of Ramadan month by predicting the visibility of the new crescent Moon. The results obtained from our experiments have shown very good accurate prediction and evaluation performance. The Random Forest and Support Vector Machine classifiers have provided promising results compared to other classifiers considered in this study in predicting the visibility of the new Moon.
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spelling pubmed-101261292023-04-26 Predicting new crescent moon visibility applying machine learning algorithms Al-Rajab, Murad Loucif, Samia Al Risheh, Yazan Sci Rep Article The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of Ramadan month within the Muslim community. This is mainly due to the imprecise observations of the new crescent Moon in different locations. Artificial intelligence and its sub-field machine learning have shown great success in their application in several fields. In this paper, we propose the use of machine learning algorithms to help in determining the start of Ramadan month by predicting the visibility of the new crescent Moon. The results obtained from our experiments have shown very good accurate prediction and evaluation performance. The Random Forest and Support Vector Machine classifiers have provided promising results compared to other classifiers considered in this study in predicting the visibility of the new Moon. Nature Publishing Group UK 2023-04-24 /pmc/articles/PMC10126129/ /pubmed/37095098 http://dx.doi.org/10.1038/s41598-023-32807-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Al-Rajab, Murad
Loucif, Samia
Al Risheh, Yazan
Predicting new crescent moon visibility applying machine learning algorithms
title Predicting new crescent moon visibility applying machine learning algorithms
title_full Predicting new crescent moon visibility applying machine learning algorithms
title_fullStr Predicting new crescent moon visibility applying machine learning algorithms
title_full_unstemmed Predicting new crescent moon visibility applying machine learning algorithms
title_short Predicting new crescent moon visibility applying machine learning algorithms
title_sort predicting new crescent moon visibility applying machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126129/
https://www.ncbi.nlm.nih.gov/pubmed/37095098
http://dx.doi.org/10.1038/s41598-023-32807-x
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