Publication2025
Unraveling Autism Through FCN Using Hierarchical Support Vector Machine (H-SVM) and Interactive Embedding-K Nearest Neighbors (InEm-KNN)
- Good Health and Well-being
- Empowered Indians
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Lecture Notes in Computer Science
- Volume
- 15614 LNCS
- Pages
- 194-208
- Published
- 1 January 2025
- DOI
- 10.1007/978-3-031-87657-8_14
- Scopus ID
- 105005570705
- ISSN
- 03029743
- Authors
- 3
- Open access
- No
- Citations
- 2
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
3 Ensure healthy lives and promote well-being for all at all ages
Hierarchical support vector machine achieves 89 percent accuracy in autism spectrum disorder detection from brain imaging.
- 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
Viksit Bharat @2047
vb-1 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Hierarchical support vector machine achieves 89 percent accuracy in autism spectrum disorder detection from brain imaging.
- 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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- 1
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Citing this record
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KIIT Deemed to be University. “Unraveling Autism Through FCN Using Hierarchical Support Vector Machine (H-SVM) and Interactive Embedding-K Nearest Neighbors (InEm-KNN)”. Kortex.