Kernel Metadata Declaration — on demand

ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.

✓ Meets every rule of the format

The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.

What the DOI identifies

IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT

10.46243/jst.2023.v8.i06.pp45-57 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2022-05-11

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 9 · pp. 45–57

Principal agents

Mrs. K. Aarati Mrs. K. Aarati (author → Author)
Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 3 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

DOI Name DOI name	10.46243/jst.2023.v8.i06.pp45-57
Referent Type referentType	Creation
Referent Sub-Type referentSubType	JournalArticle — an article in a journal
Referent Name(s) referentName(s)	IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT (PrincipalTitle, en)
Basic Metadata basicMetadata	author: Mrs. K. Aarati Mrs. K. Aarati publisher: Longman Publishers published: 2022-05-11 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 9 · pp. 45–57 language: en form: Digital · Visual · Language
Referent Identifier(s) alternateIdentifier(s)	none besides the DOI
Registration Authority registrationAuthorityCode	Crossref — issued by Crossref (member 25296); held here as a copy
Created Date issueDate	2024-02-16
relatedIdentifiers	none needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2023.v8.i06.pp45-57",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT",
            "type": "PrincipalTitle",
            "lang": "en"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2023.v8.i06.pp45-57"
        }
    ],
    "agents": [
        {
            "role": "author",
            "name": {
                "given": "Mrs. K. Aarati",
                "family": "Mrs. K. Aarati"
            },
            "sequence": "first"
        },
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2022-05-11",
        "date_type": "PublicationDate",
        "online": "2022-05-11"
    },
    "language": "en",
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "7",
        "issue": "9",
        "pages": {
            "first": "45",
            "last": "57"
        }
    },
    "links": [
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/697",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/697/628",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/697/1724",
            "purpose": "text-mining",
            "return_type": "application/xml"
        },
        {
            "url": "https://jst.org.in/admin/uploads/Batch%207.pdf",
            "purpose": "similarity-checking"
        }
    ],
    "abstract": {
        "value": "Patients depend on health insurance provided by the governmentsystems, private systems, or both to utilizethe high-priced healthcare expenses. This dependency on health insurance draws some healthcare service providers to commit insurance frauds. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Decision-Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), to detect the health insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score",
        "lang": "en"
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "start": "2022-05-11",
        "applies_to": "vor"
    },
    "references": [
        {
            "key": "ref1",
            "doi": "10.1109/icbk.2017.47",
            "unstructured": "W. Zhang and X. He, ―An anomaly detection method for Medicare fraud detection,‖ in Big Knowledge (ICBK), 2017 IEEE International Conference on. IEEE, 2017, pp. 309–314"
        },
        {
            "key": "ref2",
            "doi": "10.1109/spices52834.2022.9774071",
            "unstructured": "A. Urunkar, A. Khot, R. Bhat and N. Mudegol, \"Fraud Detection and Analysis for Insurance Claim using Machine Learning,\" 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), THIRUVANANTHAPURAM, India, 2022, pp. 406-411, doi: 10.1109/SPICES52834.2022.9774071"
        },
        {
            "key": "ref3",
            "doi": "10.1109/icmla.2016.0063",
            "unstructured": "R. A. Bauder and T. M. Khoshgoftaar, ―A probabilistic programming approach for outlier detection in healthcare claims,‖ in Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on. IEEE, 2016, pp. 347–354"
        }
    ],
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2024-02-16",
        "updated": "2026-09-17",
        "issue_number": 1,
        "source": "crossref-api",
        "source_agency": "Crossref (member 25296)"
    }
}

The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
  <referentDoiName>10.46243/jst.2023.v8.i06.pp45-57</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name primaryLanguage="en">
      <value>IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2023.v8.i06.pp45-57</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2023.v8.i06.pp45-57</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/697</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/download/697/628</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/697/1724</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://jst.org.in/admin/uploads/Batch%207.pdf</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>Mrs. K. Aarati Mrs. K. Aarati</value>
        <type>PrincipalName</type>
      </name>
      <role>Author</role>
    </principalAgent>
    <principalAgent>
      <name>
        <value>Longman Publishers</value>
        <type>PrincipalName</type>
      </name>
      <role>Publisher</role>
    </principalAgent>
    <linkedCreation>
      <name>
        <value>Journal of Science &amp; Technology</value>
        <type>PrincipalTitle</type>
      </name>
      <identifier>
        <nonUriValue>2456-5660</nonUriValue>
        <type>ISSN</type>
      </identifier>
      <referentCreationRole>Part</referentCreationRole>
      <referentCreationSequenceIdentifier>
        <value>7</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>9</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>45-57</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <language>en</language>
    <languageOfReferentContent>
      <language>en</language>
      <languageOfReferentContentType>Original</languageOfReferentContentType>
    </languageOfReferentContent>
    <creationDate>
      <date>2022-05-11</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>

Kernel Metadata Declaration — on demand

✓ Meets every rule of the format

What the DOI identifies

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

The record

The Kernel Metadata Declaration

For journals & publishers

DOI care

For authors & researchers

For institutions, sponsors & DOI operators

Smart Scholars

News

Policies

What we can register a DOI for

Journals

Books & conferences

Other research output

Funding

Verified

Elsewhere