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arXiv 2409.02150astro-ph.EPcs.LG
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危险小行星分类

Hazardous Asteroids Classification

National Central University(国立中央大学)

Thai Duy Quy, Alvin Buana, Josh Lee, Rakha Asyrofi

AI总结:

本文研究利用五种机器学习算法和五种深度学习模型,在Kaggle与NASA NeoWS两个数据集上对危险小行星进行准确分类,以寻找最优分类模型。

摘要:

危险小行星一直是人类关注的问题之一,因为坠落到地球上的小行星可能对社会造成巨大影响。监测这些天体有助于预测未来的撞击事件,但此类努力受到大量经过地球附近天体的阻碍。本项目旨在利用机器学习和深度学习对危险小行星进行准确分类。我们训练并评估了总共十种方法,包括五种机器学习算法和五种深度学习模型,以寻找能够解决该问题的合适模型。我们在两个数据集上进行了实验,一个来自Kaggle,另一个是我们从名为NeoWS的Web服务中提取的,NeoWS是NASA提供的RESTful Web服务,提供近地小行星信息,并且每天更新。总体而言,该模型在两个具有不同特征的数据集上进行了测试,以找到执行分类最准确的模型。

英文摘要:

Hazardous asteroid has been one of the concerns for humankind as fallen asteroid on earth could cost a huge impact on the society.Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth's vicinity. The aim of this project is to use machine learning and deep learning to accurately classify hazardous asteroids. A total of ten methods which consist of five machine learning algorithms and five deep learning models are trained and evaluated to find the suitable model that solves the issue. We experiment on two datasets, one from Kaggle and one we extracted from a web service called NeoWS which is a RESTful web service from NASA that provides information about near earth asteroids, it updates every day. In overall, the model is tested on two datasets with different features to find the most accurate model to perform the classification.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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