Publication2026
Capturing Short- and Long-Term Temporal Dependencies Using Bahdanau-Enhanced Fused Attention Model for Financial Data—An Explainable AI Approach
The record
- Type
- Publication
- Date
- 1 March 2026
- Level
- National
- Journal
- Fintech
- Volume
- 5
- Issue
- 1
- Published
- 1 March 2026
- DOI
- 10.3390/fintech5010004
- Scopus ID
- 105034063632
- ISSN
- 26741032
- Authors
- 4
- Open access
- Yes
- 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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
BiGRU-based attention neural network with SHAP and integrated gradients advances explainable artificial intelligence research methodology
- 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
8 Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
Stock market prediction with high R-squared scores supports informed investment decisions and economic growth planning
- 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-3.2 Digital technology and artificial intelligence
Explainable AI development enhances digital technology innovation and interpretable machine learning 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Capturing Short- and Long-Term Temporal Dependencies Using Bahdanau-Enhanced Fused Attention Model for Financial Data—An Explainable AI Approach”. Kortex.