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

Publication2022

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

The record

Type
Publication
Date
1 August 2022
Level
International — held abroad
Journal
Expert Systems
Volume
39
Issue
7
Published
1 August 2022
DOI
10.1111/exsy.12541
Scopus ID
85081541157
ISSN
02664720
Authors
7
Open access
No
Citations
74
International co-authorship
Yes
Co-author countries
CN, GB, IN, PT

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. 9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

    GoalPrimaryHigh confidenceJudged

    Deep CNN feature selection using fuzzy entropy approach achieves 99.8% gait recognition accuracy on AVAMVG dataset

    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. 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-3.2 Digital technology and artificial intelligence

    Sub-themePrimaryHigh confidenceJudged

    Biometric identification technology advances digital surveillance and security 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.

Evidence

Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.

No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.

Provenance

Which source system this record came from, how many source rows were resolved into it, and when each was last read.

Source rows resolved into this record
1

Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.

Citing this record

This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.

KIIT Deemed to be University. “A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition”. Kortex.

Explore the full record