Vayadande, Kuldeep and Bavkar, Dnyaneshwar M. and Raskar, Ishwari Rohit and Mulani, Umar Mubarak and Kanjalkar, Jyoti and Gadhave, Rajashree Tukaram and Bailke, Preeti and Bodhe, Yogesh and Patil, Ajit R. (2025) Reimagining Asteroid Risk Assessment: A Comparative Review of Advanced Machine Learning Techniques. EAI Endorsed Transactions on AI and Robotics.
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Abstract
The escalating discovery rate of Near-Earth Asteroids (NEAs) has intensified the need for advanced computational frameworks capable of evaluating their impact risks with high precision. Traditional machine learning models, while foundational for early NEA classification and trajectory prediction, in
| Item Type: | Article |
|---|---|
| Date Deposited: | 04 Mar 2026 18:36 |
| Last Modified: | 10 Apr 2026 22:31 |
| URI: | http://eprints.eai.eu/id/eprint/53220 |
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