Publication2023
Prediction of Algae Growth: A Machine Learning Perspective
The record
- Type
- Publication
- Date
- 3 August 2023
- Level
- National
- Journal
- ACM International Conference Proceeding Series
- Pages
- 109-114
- Published
- 3 August 2023
- DOI
- 10.1145/3607947.3607967
- Scopus ID
- 85175706105
- Authors
- 6
- Open access
- Yes
- Citations
- 3
- 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
Machine learning approach predicts harmful algal blooms affecting aquatic ecosystems and human health through environmental feature analysis.
- 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Algorithms including ANN, GBDT, and SVM advance research capacity for environmental monitoring and prediction.
- 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-2.5 Green economy, energy transition and climate resilience
Algal bloom prediction supports ecosystem sustainability and mitigation of environmental damage to aquatic systems.
- 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. “Prediction of Algae Growth: A Machine Learning Perspective”. Kortex.