10.46243/jst.2020.v5.i3.pp138-146 registered
Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data
Resolves to https://www.jst.org.in/index.php/pub/article/view/420
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2020.v5.i3.pp138-146
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 c28d54b28d721dc7…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data (PrincipalTitle)
Published 2020-05-29
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 5 · issue 3 · pages 138–146
Agents
- N.Adityasundar (author)
- T.SaiAbhigna (author)
- B.Lakshman (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2020.v5.i3.pp138-146
Abstract
:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2020.v5.i3.pp138-146 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: N.Adityasundar author: T.SaiAbhigna author: B.Lakshman publisher: Longman Publishers published: 2020-05-29 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 5 · no. 3 · pp. 138–146 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2020-05-29 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
Recommended for a journal article and not in this record: the reference list.
The System Metadata Declaration (JSON) · the Kernel Metadata Declaration (XML) · what each sub-type needs
History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 60 fields set · sha256 fe01d939e7dc… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
Machine-readable: the history as JSON, with the full record after each change.
