Intelligent System for Prediction of Potentially Hazardous Nearest Earth Objects Using Machine Learning


  • Pooja Bagane Symbiosis Institute of Technology, Constituent of Symbiosis International University, Pune
  • Srinivasa Rao Kandula Professor, Department of ECE, Dhanekula Institute of Engineering and Technology, Vijyawada, Andhra Pradesh, India
  • Aditi Saxena Department of Electronics and Communication Engineering, GLA University Mathura
  • Sanjit Das Associate Professor, School of Advanced Sciences, VIT Chennai
  • A. Deepak Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu. India
  • Sampathirao Govinda Rao Professor, Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology (Griet)


Asteroids, Prediction, NEOs, Machine Learning, Hazardous, Classification, Logistic Regression


Large potentially hazardous NEOs can cause a worldwide disaster in the occasion of a planetary colliding. These collisions with Earth are not uncommon. Every year, hundreds of asteroids hit the surface of our planet, the majority of which are relatively small to cause any worry. But occasionally, big rocks can collide and harm anything. In order to categorize the population of NEAs as potentially harmful or non-hazardous, this study proposes a method that uses different algorithms to learn complicated representations that are present in the distribution of accessible asteroid orbital data.


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How to Cite

Bagane, P. ., Kandula, S. R. ., Saxena, A. ., Das, S. ., Deepak, A. ., & Rao, S. G. . (2023). Intelligent System for Prediction of Potentially Hazardous Nearest Earth Objects Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 71–80. Retrieved from



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