Publication2026
Machine Learning-Based Career Recommendation System Using Academic and Behavioral Analytics
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- International — held abroad
- Journal
- Esic 2026 Proceedings 6th International Conference on Emerging Systems and Intelligent Computing
- Pages
- 822-827
- Published
- 1 January 2026
- DOI
- 10.1109/esic68176.2026.11495729
- Scopus ID
- 105040801683
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- Yes
- Co-author countries
- IN, US
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
4.4 Substantially increase the number of youth and adults with relevant skills for employment, decent jobs and entrepreneurship
Machine learning career recommendation system identifies suitable paths using academic performance and behavioral attributes for evidence-based student guidance.
- 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
Educational data mining supporting employment readiness through personalized career pathway recommendations.
- 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
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-1.1 Education and lifelong learning
Higher education career guidance through AI-powered recommendation systems supporting student skill-to-career alignment.
- 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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Provenance
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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. “Machine Learning-Based Career Recommendation System Using Academic and Behavioral Analytics”. Kortex.