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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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Detalles Bibliográficos
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
Descripción
Sumario: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.