Publication2025
Classification and Comparative Analysis of Hazardous Asteroids Using Machine Learning
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- 3rd IEEE International Conference on Networks Multimedia and Information Technology Nmitcon 2025
- Published
- 1 January 2025
- DOI
- 10.1109/nmitcon65824.2025.11188178
- Scopus ID
- 105020796294
- Authors
- 6
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Comparative machine learning analysis using random forest, naive Bayes and K-nearest neighbors for near-Earth asteroid hazard classification demonstrates predictive modeling achieving 99.99 percent accuracy.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.5 Space, defence and strategic technology
Asteroid classification research using advanced analytics demonstrates space technology and strategic technology research.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Classification and Comparative Analysis of Hazardous Asteroids Using Machine Learning”. Kortex.