Publication2023
Tree Based Fault Classification in Underground Cable
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- Procedia Computer Science
- Volume
- 218
- Pages
- 524-531
- Published
- 1 January 2023
- DOI
- 10.1016/j.procs.2023.01.034
- Scopus ID
- 85163771266
- ISSN
- 18770509
- Authors
- 4
- Open access
- Yes
- Citations
- 14
- 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
Develops machine learning classification algorithms including decision trees and random forest achieving 98.8% accuracy for fault type detection and location identification in underground cables
- 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-3.2 Digital technology and artificial intelligence
Artificial intelligence application for electrical infrastructure condition monitoring improves grid reliability and fault management capabilities
- 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Tree Based Fault Classification in Underground Cable”. Kortex.