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
Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms
10.46243/jst.2021.v6.i06.pp94-102 · JournalArticle — an article in a journal · Digital · Visual · Language · en
Published 2021-12-18
Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 06 · pp. 94–102
Principal agents
- Dr. R. Pradeep Kumar Reddy (author → Author)
- C. Naga Raju (author → Author)
- Longman Publishers (publisher → Publisher)
Also in the record, outside the Kernel: the abstract, the licence, 4 links, 16 references. Source: Crossref (member 25296), registered 2026-09-08, last deposited 2026-09-20.
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.2021.v6.i06.pp94-102 |
| Referent Type referentType | Creation |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal |
| Referent Name(s) referentName(s) | Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms (PrincipalTitle, en) |
| Basic Metadata basicMetadata | author: Dr. R. Pradeep Kumar Reddy author: C. Naga Raju publisher: Longman Publishers published: 2021-12-18 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 06 · pp. 94–102 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 | 2026-09-08 |
| relatedIdentifiers | none needed — the descriptive metadata is in this record |
The record
{
"format": "smartscholars-doi-metadata/1.0",
"doi": "10.46243/jst.2021.v6.i06.pp94-102",
"referent": "Creation",
"type": "JournalArticle",
"structural_type": "Digital",
"modes": [
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"characters": [
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"titles": [
{
"value": "Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms",
"type": "PrincipalTitle",
"lang": "en"
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],
"identifiers": [
{
"type": "DOI",
"value": "10.46243/jst.2021.v6.i06.pp94-102"
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"agents": [
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"role": "author",
"name": {
"given": "Dr. R. Pradeep",
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},
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{
"role": "author",
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{
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"name": {
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"dates": {
"published": "2021-12-18",
"date_type": "PublicationDate",
"online": "2021-12-18"
},
"language": "en",
"container": {
"type": "Journal",
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"pages": {
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},
"links": [
{
"url": "https://www.jst.org.in/index.php/pub/article/view/520",
"return_type": "text/html",
"primary": true
},
{
"url": "https://www.jst.org.in/index.php/pub/article/download/520/461",
"purpose": "text-mining",
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{
"url": "https://www.jst.org.in/index.php/pub/article/download/520/2513",
"purpose": "text-mining",
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{
"url": "https://www.jst.org.in/index.php/pub/article/view/520/461",
"purpose": "similarity-checking"
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],
"abstract": {
"value": "The style of handwriting varies from person to person. Handwritten numbers are not always the same size, orientation and width. To develop a system to understand this, the machine recognizes handwritten digit images and classifies them into 10 digits (from 0 to 9).Handwritten digit recognition is a technology which is used for automatic recognizing and detecting handwritten digital data through various machine learning models. This paper uses a different machine learning algorithms to improve productivity and a variety of models to reduce complexity. Machine Learning is an artificial intelligence application which learns from previous experiences and it automatically improves with the previous experiences. This paper is about recognizing handwritten digits from 0 to 9 from the well-known Modified National Institute of Standards and Technology(MNIST) dataset, then comparison takes place between machine learning algorithms like Support Vector Machine(SVM), logistic regression, K-Nearest Neighbor (KNN) and deep learning algorithm like CNN",
"lang": "en"
},
"license": {
"url": "https://creativecommons.org/licenses/by/4.0/",
"start": "2021-12-18",
"applies_to": "vor"
},
"references": [
{
"key": "ref1",
"doi": "10.1007/s10773-019-04124-5",
"unstructured": "Wang, Y., Wang, R., Li, D. et al. Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbour Algorithm. Int J Theor Phys 58, 2331–2340 (2019)"
},
{
"key": "ref2",
"doi": "10.1109/peeic47157.2019.8976601",
"unstructured": "I. J. S. G. D. G. Anchit Shrivastava, \"Handwritten Digit Recognition Using Machine Learning : A Review,\" pp. 322-326, 2019"
},
{
"key": "ref3",
"doi": "10.11591/ijece.v9i5.pp4446-4451",
"unstructured": "Assegie, Tsehay & Nair, Pramod. (2019). Handwritten digits recognition with decision tree classification: a machine learning approach. International Journal of Electrical and Computer Engineering (IJECE). 9. 4446. 10.11591/ijece.v9i5.pp4446-4451. K. Elissa, “Title of paper if known,” unpublished"
},
{
"key": "ref4",
"doi": "10.1109/icicta49267.2019.00145",
"unstructured": "D. Ge, X. Yao, W. Xiang, X. Wen and E. Liu, \"Design of High Accuracy Detector for MNIST Handwritten Digit Recognition Based on Convolutional Neural Network,\" 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA), Xiangtan, China, 2019, pp. 658-662, doi: 10.1109/ICICTA49267.2019.00145"
},
{
"key": "ref5",
"unstructured": "Al-Wzwazy, Haider. (2016). Handwritten Digit Recognition Using Convolutional Neural Networks. International Journal of Innovative Research in Computer and Communication Engineering"
},
{
"key": "ref6",
"doi": "10.1109/comitcon.2019.8862451",
"unstructured": "S. Ray, \"A Quick Review of Machine Learning Algorithms,\" IEEE, pp. 35-39, 2019"
},
{
"key": "ref7",
"unstructured": "B. K. Vijayalaxmi R Rudraswamimath, \"Handwritten Digit Recognition using CNN,\" International Journal of Innovative Science and Research Technology (IJISRT), vol. 4, no. 6, pp. 182-187, June 2019"
},
{
"key": "ref8",
"doi": "10.1109/ewdts50664.2020.9224822",
"unstructured": "D. T. Zufar Kayumov, \"Convolution Neural Network Learning Features for Handwritten Digit Recognition,\" IEEE, 2020"
},
{
"key": "ref9",
"doi": "10.4236/jilsa.2017.92003",
"unstructured": "Khan, H. (2017) MCS HOG Features and SVM Based Handwritten Digit Recognition System. Journal of Intelligent Learning Systems and Applications, 9, 21-33"
},
{
"key": "ref10",
"doi": "10.1109/icaee48663.2019.8975496",
"unstructured": "S. S. M. A. B. S. Fathima Siddique, \"Recognition of Handwritten Digit using Convolutional Neural Network in python with Tensorflow and Comparison of Performance for Various Hidden Layers,\" IEEE, pp. 541-546, 2019"
},
{
"key": "ref11",
"doi": "10.1109/icccet.2011.5762513",
"unstructured": "E.,. S. J.Pradeep, \"Neural Network based Handwritten Character Recognition system without feature extraction,\" IEEE,International Conference on Computer, Communication and Electrical Technology–ICCCET, pp. 40-44, 2011. Comparative Study Of Efficacy Of Atorvastatin 40 Mg Daily Rosuvastatin 20 Mg"
},
{
"key": "ref12",
"doi": "10.1109/icaiis49377.2020.9194797",
"unstructured": "P. H.,. a. D. L. Junyi Tang, \"Adhesive Handwritten Digit Recognition Algorithm Based on Improved Convolutional Neural Network,\" IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), pp. 388-392, 2020"
},
{
"key": "ref13",
"doi": "10.1109/incos45849.2019.8951342",
"unstructured": "P. a. P. S. M.Rajalakshmi, \"Pattern Recognition-Recognition of Handwritten Document Using Convolutional Neural Networks,\" IEEE, 2019"
},
{
"key": "ref14",
"doi": "10.21275/art20203995",
"unstructured": "B. Mahesh, \"Machine Learning Algorithms-A Review,\" International Journal of Science and Research (IJSR), vol. 9, no. 1, pp. 381-386, 2018"
},
{
"key": "ref15",
"doi": "10.1109/acsat.2014.36",
"unstructured": "A. M. Z.,. A. M. Z. Mustafa Ali Abuzaraida, \"Online Recognition System for Handwritten Hindi Digits Based On Matching Alignment Algorithm,\" IEEE, 2015"
},
{
"key": "ref16",
"doi": "10.1109/icecco48375.2019.9043266",
"unstructured": "B. K. K. M. A. A. Nurseitov Daniyar, \"Classification of handwritten names of cities using various deep learning models,\" IEEE,15th International Conference on Electronics Computer and Computation (ICECCO), 2019"
}
],
"record": {
"registrant": "Longman Publishers",
"registered": "2026-09-08",
"updated": "2026-09-20",
"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.2021.v6.i06.pp94-102</referentDoiName>
<primaryReferentType>Creation</primaryReferentType>
<registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
<issueDate>2026-10-04</issueDate>
<issueNumber>1</issueNumber>
<referentCreation>
<name primaryLanguage="en">
<value>Comparative Analysis of Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms</value>
<type>PrincipalTitle</type>
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<type>DOI</type>
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<identifier>
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<type>URI</type>
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<identifier>
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<type>URI</type>
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<identifier>
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<type>URI</type>
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<identifier>
<uri>https://www.jst.org.in/index.php/pub/article/view/520/461</uri>
<type>URI</type>
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<structuralType>Digital</structuralType>
<mode>Visual</mode>
<character>Language</character>
<type>JournalArticle</type>
<principalAgent>
<name>
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<type>PrincipalName</type>
</name>
<role>Author</role>
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<principalAgent>
<name>
<value>C. Naga Raju</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Longman Publishers</value>
<type>PrincipalName</type>
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<role>Publisher</role>
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<linkedCreation>
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<type>PrincipalTitle</type>
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<type>ISSN</type>
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<referentCreationRole>Part</referentCreationRole>
<referentCreationSequenceIdentifier>
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<type>VolumeNumber</type>
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<type>IssueNumber</type>
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<type>PageNumber</type>
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<language>en</language>
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<language>en</language>
<languageOfReferentContentType>Original</languageOfReferentContentType>
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<creationDate>
<date>2021-12-18</date>
<creationDateType>PublicationDate</creationDateType>
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</kernelMetadata>
