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

Publication2025

A novel RX-RZ Entangled Feature Map Kernel for Fraud Detection using Quantum Support Vector Machine Classifier

The record

Type
Publication
Date
1 January 2025
Level
International — held abroad
Journal
2025 5th IEEE International Conference on Applied Electromagnetics Signal Processing and Communication Aespc 2025
Published
1 January 2025
DOI
10.1109/aespc67542.2025.11326762
Scopus ID
105033146244
Authors
3
Open access
No
Citations
0
International co-authorship
Yes
Co-author countries
IN, SE

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

    TargetPrimaryHigh confidenceJudged

    Quantum machine learning framework with novel entangled feature maps demonstrates advanced quantum computing research for fraud detection.

    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

    Quantum-assisted AI for fraud analytics represents cutting-edge artificial intelligence and quantum technology integration.

    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 novel RX-RZ Entangled Feature Map Kernel for Fraud Detection using Quantum Support Vector Machine Classifier”. Kortex.

Explore the full record