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Machine learning techniques for classifying dangerous asteroids

There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Therefore, in this article, with the hyperparameters tuning of Extra Tree, Random Forest, Light Gradient Boost...

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Autores principales: Malakouti, Seyed Matin, Menhaj, Mohammad Bagher, Suratgar, Amir Abolfazl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480302/
https://www.ncbi.nlm.nih.gov/pubmed/37680366
http://dx.doi.org/10.1016/j.mex.2023.102337
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author Malakouti, Seyed Matin
Menhaj, Mohammad Bagher
Suratgar, Amir Abolfazl
author_facet Malakouti, Seyed Matin
Menhaj, Mohammad Bagher
Suratgar, Amir Abolfazl
author_sort Malakouti, Seyed Matin
collection PubMed
description There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Therefore, in this article, with the hyperparameters tuning of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost, the hazards of asteroids around the Earth were classified, and the results of ROC Curves for these algorithms were compared. • Reviewing the list of NASA-certified asteroids classified as the nearest Earth object; • Investigating the risk of asteroids with the help of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost; • Comparing the performance of machine learning algorithms in the classification of high-risk asteroids.
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spelling pubmed-104803022023-09-07 Machine learning techniques for classifying dangerous asteroids Malakouti, Seyed Matin Menhaj, Mohammad Bagher Suratgar, Amir Abolfazl MethodsX Physics and Astronomy There is an infinite number of objects in outer space, and these objects and asteroids might be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Therefore, in this article, with the hyperparameters tuning of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost, the hazards of asteroids around the Earth were classified, and the results of ROC Curves for these algorithms were compared. • Reviewing the list of NASA-certified asteroids classified as the nearest Earth object; • Investigating the risk of asteroids with the help of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost; • Comparing the performance of machine learning algorithms in the classification of high-risk asteroids. Elsevier 2023-08-19 /pmc/articles/PMC10480302/ /pubmed/37680366 http://dx.doi.org/10.1016/j.mex.2023.102337 Text en © 2023 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Physics and Astronomy
Malakouti, Seyed Matin
Menhaj, Mohammad Bagher
Suratgar, Amir Abolfazl
Machine learning techniques for classifying dangerous asteroids
title Machine learning techniques for classifying dangerous asteroids
title_full Machine learning techniques for classifying dangerous asteroids
title_fullStr Machine learning techniques for classifying dangerous asteroids
title_full_unstemmed Machine learning techniques for classifying dangerous asteroids
title_short Machine learning techniques for classifying dangerous asteroids
title_sort machine learning techniques for classifying dangerous asteroids
topic Physics and Astronomy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480302/
https://www.ncbi.nlm.nih.gov/pubmed/37680366
http://dx.doi.org/10.1016/j.mex.2023.102337
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