Publication2024
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
The record
- Type
- Publication
- Date
- 1 June 2024
- Level
- National
- Journal
- Journal of Discrete Mathematical Sciences and Cryptography
- Volume
- 27
- Issue
- 4
- Pages
- 1233-1246
- Published
- 1 June 2024
- DOI
- 10.47974/jdmsc-1978
- Scopus ID
- 85197740854
- ISSN
- 09720529
- Authors
- 5
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
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
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Ensemble learning methodology for fraud detection in imbalanced datasets represents novel research advancement in machine learning security applications
- 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
16 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Credit card fraud detection technology supports reduction of financial crime and corruption through advanced computational security measures
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Cybersecurity and fraud detection research supports national security through protecting digital payment systems and financial transaction integrity
- 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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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges”. Kortex.