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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2023

MFEMANet: an effective disaster image classification approach for practical risk assessment

The record

Type
Publication
Date
1 September 2023
Level
International — held abroad
Journal
Machine Vision and Applications
Volume
34
Issue
5
Published
1 September 2023
DOI
10.1007/s00138-023-01430-1
Scopus ID
85165733844
ISSN
09328092
Authors
3
Open access
No
Citations
13
International co-authorship
Yes
Co-author countries
IN, TW

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

  1. 11 Make cities and human settlements inclusive, safe, resilient and sustainable

    GoalPrimaryHigh confidenceJudged

    Machine learning approach for disaster image classification enables rapid risk assessment and rescue planning in disaster-affected regions.

    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

  2. 9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending

    TargetSecondaryHigh confidenceJudged

    Research on disaster image classification using CNN and ensemble methods contributes to innovation in artificial intelligence.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

  3. 17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing

    TargetSecondaryHigh confidenceRule-derivedrule sdg17-structural-v1

    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

  1. vb-4.4 Public safety, cybersecurity and national security

    Sub-themePrimaryHigh confidenceJudged

    Disaster risk assessment technology supports public safety and emergency response capabilities.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “MFEMANet: an effective disaster image classification approach for practical risk assessment”. Kortex.

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