Publication2024
Enhancing Fish Species Recognition: A Holistic Performance Evaluation of Deep Learning Models
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 2nd IEEE International Conference on Data Science and Network Security Icdsns 2024
- Published
- 1 January 2024
- DOI
- 10.1109/icdsns62112.2024.10691150
- Scopus ID
- 85206580707
- Authors
- 6
- Open access
- No
- Citations
- 0
- 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
14 Conserve and sustainably use the oceans, seas and marine resources for sustainable development
Deep learning models (ResNet50, Inception V3) achieve 99% accuracy for fish species identification, supporting biodiversity assessment and sustainable fisheries management.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- 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
12 Ensure sustainable consumption and production patterns
Fish species recognition supports sustainable fisheries practices and responsible aquatic resource management.
- 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.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.1 Agriculture and the rural economy
Fisheries biodiversity assessment supports sustainable aquatic economy.
- 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. “Enhancing Fish Species Recognition: A Holistic Performance Evaluation of Deep Learning Models”. Kortex.