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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.

Reads the DOI's public Crossref record; nothing is stored. Until Smart Scholars is accredited, every Declaration is marked as a draft in the XML itself.

✓ 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

Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.

10.46243/jst.2025.v10.i04.pp27-33 · JournalArticle — an article in a journal · Digital · Visual · Language

Published 2025-04-21

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 4 · pp. 27–33

Principal agents

  • Lakshmi Prasad Rongali (author → Author)
  • Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 2 links, 22 references. Source: Crossref (member 25296), registered 2025-05-23, last deposited 2026-09-07.

⬇ 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.2025.v10.i04.pp27-33
Referent Type
referentType
Creation
Referent Sub-Type
referentSubType
JournalArticle — an article in a journal
Referent Name(s)
referentName(s)
Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids. (PrincipalTitle)
Basic Metadata
basicMetadata
author: Lakshmi Prasad Rongali
publisher: Longman Publishers
published: 2025-04-21
part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 4 · pp. 27–33
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
2025-05-23
relatedIdentifiersnone needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2025.v10.i04.pp27-33",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.",
            "type": "PrincipalTitle"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2025.v10.i04.pp27-33"
        }
    ],
    "agents": [
        {
            "role": "author",
            "name": {
                "given": "",
                "family": "Lakshmi Prasad Rongali"
            },
            "sequence": "first"
        },
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2025-04-21",
        "date_type": "PublicationDate",
        "online": "2025-04-21"
    },
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "10",
        "issue": "4",
        "pages": {
            "first": "27",
            "last": "33"
        }
    },
    "links": [
        {
            "url": "https://jst.org.in/index.php/pub/article/view/1275",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://jst.org.in/index.php/pub/article/download/1275/992",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        }
    ],
    "abstract": {
        "value": "The research presents an analysis of the enhancement process of AI-driven DevOps in grid management by modifyinganomaly detection, predictive maintenance and entire system effectiveness. It involves an AI driven continuousfeedback loop between these two areas, to get the best delivery and upgrades of the AI models. This collaborationhelps improve system reliability as well as speed up the deployment of needed updates, requiring the smart grid to runas close to optimal as possible."
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0",
        "start": "2025-04-21",
        "applies_to": "unspecified"
    },
    "references": [
        {
            "key": "ref1",
            "doi": "10.1177/18479790211032920",
            "unstructured": "Meliani, M., Barkany, A.E., Abbassi, I.E., Darcherif, A.M. and Mahmoudi, M., 2021. Energy management in the smart grid: State-ofthe-art and future trends. International Journal of Engineering Business Management, 13, p.18479790211032920"
        },
        {
            "key": "ref2",
            "doi": "10.1109/access.2021.3131502",
            "unstructured": "Kumar, A., Alaraj, M., Rizwan, M. and Nangia, U., 2021. Novel AI based energy management system for smart grid with RES integration. IEEE Access, 9, pp.162530-162542"
        },
        {
            "key": "ref3",
            "doi": "10.1007/s43681-021-00132-6",
            "unstructured": "Keleko, A.T., Kamsu-Foguem, B., Ngouna, R.H. and Tongne, A., 2022. Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis. AI and Ethics, 2(4), pp.553-577"
        },
        {
            "key": "ref4",
            "unstructured": "Tyagi, A., 2021. Intelligent DevOps: Harnessing Artificial Intelligence to Revolutionize CI/ CD Pipelines and Optimize Software Delivery Lifecycles. Journal of Emerging Technologies and Innovative Research, 8, pp.367-385"
        },
        {
            "key": "ref5",
            "doi": "10.1145/3711118",
            "unstructured": "Zha, D., Bhat, Z.P., Lai, K.H., Yang, F., Jiang, Z., Zhong, S. and Hu, X., 2025. Data-centric artificial intelligence: A survey. ACM Computing Surveys, 57(5), pp.1-42"
        },
        {
            "key": "ref6",
            "doi": "10.1155/2023/8134627",
            "unstructured": "Berhane, T., Melese, T., Walelign, A. and Mohammed, A., 2023. A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model. Mathematical Problems in Engineering, 2023(1), p.8134627"
        },
        {
            "key": "ref7",
            "doi": "10.2172/2008362",
            "unstructured": "Walker, C.M., Agarwal, V., Nistor, J., Ramuhalli, P. and Muhheim, M., 2023. Assessment of Cloud-Based Applications Enabling a Scalable Risk-Informed Predictive Maintenance Strategy Across the Nuclear Fleet (No. INL/RPT-23- 74696). Idaho National Laboratory (INL), Idaho Falls, ID (United States)"
        },
        {
            "key": "ref8",
            "unstructured": "Hossain, M.E., Tarafder, M.T.R., Ahmed, N., Al Noman, A., Sarkar, M.I. and Hossain, Z., 2023. Integrating AI with Edge Computing and Cloud Services for Real-Time Data Processing and Decision Making. International Journal of Multidisciplinary Sciences and Arts, 2(4), pp.252-261"
        },
        {
            "key": "ref9",
            "doi": "10.1109/jiot.2021.3125885",
            "unstructured": "Borghesi, A., Burrello, A. and Bartolini, A., 2021. Examon-x: a predictive maintenance framework Lakshmi Prasad Rongali: Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids for automatic monitoring in industrial iot systems. IEEE Internet of Things Journal, 10(4), pp.2995-3005"
        },
        {
            "key": "ref10",
            "doi": "10.2139/ssrn.5102369",
            "unstructured": "Tanikonda, A., Katragadda, S.R., Peddinti, S.R. and Pandey, B.K., 2021. Integrating AI-Driven Insights into DevOps Practices. Journal of Science & Technology, 2(1)"
        },
        {
            "key": "ref11",
            "doi": "10.36548/rrrj.2023.2.001",
            "unstructured": "Jha, R.K., 2023. Cybersecurity and confidentiality in smart grid for enhancing sustainability and reliability. Recent Research Reviews Journal, 2(2), pp.215-241"
        },
        {
            "key": "ref12",
            "doi": "10.1016/j.rser.2022.112128",
            "unstructured": "Ahmad, T., Madonski, R., Zhang, D., Huang, C. and Mujeeb, A., 2022. Data-driven probabilistic machine learning in sustainable smart energy/ smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, p.112128"
        },
        {
            "key": "ref13",
            "doi": "10.55248/gengpi.6.0125.0229",
            "unstructured": "Vadde, B.C. and Munagandla, V.B., 2022. AI-Driven Automation in DevOps: Enhancing Continuous Integration and Deployment. International Journal of Advanced Engineering Technologies and Innovations, 1(3), pp.183-193"
        },
        {
            "key": "ref14",
            "doi": "10.3390/s21216978",
            "unstructured": "Moreno Escobar, J.J., Morales Matamoros, O., Tejeida Padilla, R., Lina Reyes, I. and Quintana Espinosa, H., 2021. A comprehensive review on smart grids: Challenges and opportunities. Sensors, 21(21), p.6978"
        },
        {
            "key": "ref15",
            "doi": "10.1109/tnsm.2021.3050148",
            "unstructured": "Jakaria, A.H.M., Rahman, M.A. and Gokhale, A., 2021. Resiliency-aware deployment of SDN in smart grid SCADA: A formal synthesis model. IEEE Transactions on Network and Service Management, 18(2), pp.1430-1444"
        },
        {
            "key": "ref16",
            "unstructured": "Hammad, A. and Abu-Zaid, R., 2024. Applications of AI in Decentralized Computing Systems: Harnessing Artificial Intelligence for Enhanced Scalability, Efficiency, and Autonomous Decision-Making in Distributed Architectures. Applied Research in Artificial Intelligence and Cloud Computing, 7, pp.161-187"
        },
        {
            "key": "ref17",
            "doi": "10.1016/j.ijepes.2021.107772",
            "unstructured": "Sevilla, F.R.S., Liu, Y., Barocio, E., Korba, P., Andrade, M., Bellizio, F., Bos, J., Chaudhuri, B., Chavez, H., Cremer, J. and Eriksson, R., 2022. State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data. International journal of electrical power & energy systems, 137, p.107772"
        },
        {
            "key": "ref18",
            "doi": "10.1016/j.comnet.2022.109341",
            "unstructured": "Barmpounakis, S., Maroulis, N., Koursioumpas, N., Kousaridas, A., Kalamari, A., Kontopoulos, P. and Alonistioti, N., 2022. AI-driven, QoS prediction for V2X communications in beyond 5G systems. Computer Networks, 217, p.109341"
        },
        {
            "key": "ref19",
            "doi": "10.1007/s40745-021-00362-9",
            "unstructured": "Samariya, D. and Thakkar, A., 2023. A comprehensive survey of anomaly detection algorithms. Annals of Data Science, 10(3), pp.829-850"
        },
        {
            "key": "ref20",
            "doi": "10.1109/access.2024.3365586",
            "unstructured": "Kisten, M., Ezugwu, A.E.S. and Olusanya, M.O., 2024. Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities. IEEE Access, 12, pp.24348-24367"
        },
        {
            "key": "ref21",
            "doi": "10.2172/2212844",
            "unstructured": "Allan, B., Oldfield, R., Doutriaux, C., Lewis, K., Ahrens, J., Sims, B., Sweeney, C., Banesh, D. and Wofford, Q., 2023. Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554) (No. SAND-2023-08642). Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)"
        },
        {
            "key": "ref22",
            "doi": "10.3390/asi4010015",
            "unstructured": "Al-Marsy, A., Chaudhary, P. and Rodger, J.A., 2021. A model for examining challenges and opportunities in use of cloud computing for health information systems. Applied System Innovation, 4(1), p.15"
        }
    ],
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2025-05-23",
        "updated": "2026-09-07",
        "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.2025.v10.i04.pp27-33</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name>
      <value>Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2025.v10.i04.pp27-33</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2025.v10.i04.pp27-33</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://jst.org.in/index.php/pub/article/view/1275</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://jst.org.in/index.php/pub/article/download/1275/992</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>Lakshmi Prasad Rongali</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>10</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>4</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>27-33</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <creationDate>
      <date>2025-04-21</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>
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