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
A Hybrid Framework For Travel Advice System Using Big Data And AI
10.46243/jst.2023.v8.i07.pp163-168 · JournalArticle — an article in a journal · Digital · Visual · Language · en
Published 2023-08-07
Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 163–168
Principal agents
- Nusrath Zeeshan Nusrath Zeeshan (author → Author)
- Longman Publishers (publisher → Publisher)
Also in the record, outside the Kernel: the abstract, the licence, 4 links, 9 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.
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.i07.pp163-168 |
| Referent Type referentType | Creation |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal |
| Referent Name(s) referentName(s) | A Hybrid Framework For Travel Advice System Using Big Data And AI (PrincipalTitle, en) |
| Basic Metadata basicMetadata | author: Nusrath Zeeshan Nusrath Zeeshan publisher: Longman Publishers published: 2023-08-07 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 163–168 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.i07.pp163-168",
"referent": "Creation",
"type": "JournalArticle",
"structural_type": "Digital",
"modes": [
"Visual"
],
"characters": [
"Language"
],
"titles": [
{
"value": "A Hybrid Framework For Travel Advice System Using Big Data And AI",
"type": "PrincipalTitle",
"lang": "en"
}
],
"identifiers": [
{
"type": "DOI",
"value": "10.46243/jst.2023.v8.i07.pp163-168"
}
],
"agents": [
{
"role": "author",
"name": {
"given": "Nusrath Zeeshan",
"family": "Nusrath Zeeshan"
},
"sequence": "first"
},
{
"role": "publisher",
"name": {
"org": "Longman Publishers"
}
}
],
"dates": {
"published": "2023-08-07",
"date_type": "PublicationDate",
"online": "2023-08-07"
},
"language": "en",
"container": {
"type": "Journal",
"titles": [
{
"value": "Journal of Science & Technology",
"type": "PrincipalTitle"
}
],
"identifiers": [
{
"type": "ISSN",
"value": "2456-5660",
"medium": "electronic"
}
],
"volume": "8",
"issue": "7",
"pages": {
"first": "163",
"last": "168"
}
},
"links": [
{
"url": "https://www.jst.org.in/index.php/pub/article/view/761",
"return_type": "text/html",
"primary": true
},
{
"url": "https://www.jst.org.in/index.php/pub/article/download/761/685",
"purpose": "text-mining",
"return_type": "application/pdf"
},
{
"url": "https://www.jst.org.in/index.php/pub/article/download/761/1742",
"purpose": "text-mining",
"return_type": "application/xml"
},
{
"url": "https://jst.org.in/admin/uploads/NUSRATH%20ZEESHAN%20(21S41F0044).pdf",
"purpose": "similarity-checking"
}
],
"abstract": {
"value": "In recent years, with the development of the internet and technology, the tourism industry has seen a significant increase in tourist numbers. The growing demand for personalized travel experiences has led to the development of travel advice systems for tourism. This helps travel agents find suitable travel destinations for clients, especially those unfamiliar with the location. Advisory systems are becoming more common in everyday activities like social networking and online buying. A hybrid framework for a travel advice system is proposed based on big data and artificial intelligence. The main aim of the system is to provide tourists with personalized travel planning based on user preferences and historical data. This allows the user to quickly locate what they are seeking for without wasting time or effort. It combines the strength of a content-based and collaborative filtering approach. To improve user affinity relationships and the quality of recommendations in the travel industry, a common recommendation filtering algorithm based on designations and user preferences has been proposed. Context-aware advice systems combine software computing and data mining to incorporate user profiles, social media history, and POI (points of interest) data. Suggestion system for a list of tourist attractions adapted to the preferences of tourists. Also acts as a travel planner by developing a detailed program that includes a multi-level framework for the travel advice system. Based on the traveler’s experiences, the ratings (reviews) were also collected and analyzed to make better decisions for new travelers that advise tourist travel locations based on their previously rated venues. The algorithm searches the database for travel opportunities and uses text-mining techniques to find places of interest. the application of intelligent e-tourism consultation in tourism, focusing on interfaces, consultation algorithms, characteristics, and techniques of artificial intelligence. The goal aims to develop a hybrid travel advisory system that leverages intelligent e-tourism advice in the travel industry, focusing on interfaces and recommendations based on big data and artificial intelligence techniques",
"lang": "en"
},
"license": {
"url": "https://creativecommons.org/licenses/by/4.0/",
"start": "2023-08-07",
"applies_to": "vor"
},
"references": [
{
"key": "ref1",
"doi": "10.3727/1098305031436980",
"unstructured": "S. Loh, F. Lorenzi, R. Saldana, and D. Lichtnow, A ˜ tourism recommender system based on collaboration and text analysis, Information Technology & Tourism, vol. 6, no. 3, pp. 157–165, 2003"
},
{
"key": "ref2",
"doi": "10.1109/fuzzy.2011.6007604",
"unstructured": "G. Fenza, E. Fischetti, D. Furno, and V. Loia, A hybrid context-aware system for tourist guidance based on collaborative filtering, in Proc. IEEE Int. Conf. Fuzzy Systems, Taipei, China, 2011, pp. 131–138"
},
{
"key": "ref3",
"doi": "10.5281/zenodo.5557889",
"unstructured": "Sadeghi Elham, & Honarvar Alireza. (2021). A Tourism Recommender System using CF approach and Matrix Factorization. Zenodo. https://doi.org/10.5281/zenodo.5557889"
},
{
"key": "ref4",
"doi": "10.1016/j.procs.2020.03.047",
"unstructured": "Alrasheed H., Alzeer A., Alhowimel A., Shameri N., Althyabi A. A Multi-Level Tourism Destination Recommender System (2020) Procedia Computer Science, 170, pp. 333-340"
},
{
"key": "ref5",
"doi": "10.26599/bdma.2020.9020015",
"unstructured": "K. A. Fararni, F. Nafis, B. Aghoutane, A. Yahyaouy, J. Riffi and A. Sabri, “Hybrid recommender system for tourism based on big data and AI: A conceptual framework,” in Big Data Mining and Analytics, vol. 4, no. 1, pp. 47-55, March 2021, doi: 10.26599/BDMA.2020.9020015"
},
{
"key": "ref6",
"doi": "10.1016/j.eswa.2014.06.007",
"unstructured": "J. Borras, A. Moreno, and A. Valls, Intelligent tourism ` recommender systems: A survey, Expert Systems with Applications, vol. 41, no. 16, pp. 7370–7389, 2014"
},
{
"key": "ref7",
"doi": "10.1016/j.procs.2018.07.200",
"unstructured": "O. Boulaalam, B. Aghoutane, D. El Ouadghiri, A. Moumen, and M. L. C. Malinine, Proposal of a big data system based on the recommendation and profiling techniques for intelligent management of Moroccan tourism, Procedia Computer Science, vol. 134, pp. 346–351, 2018"
},
{
"key": "ref8",
"doi": "10.1007/s10115-017-1056-y",
"unstructured": "K. H. Lim, J. Chan, C. Leckie, and S. Karunasekera, Personalized tour recommendation based on user interests and points of interest visit duration’s, in Proc. 24th Int. Joint Conf. Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 1778–1784"
},
{
"key": "ref9",
"unstructured": "A. Menk, L. Sebastian, and R. Ferreira, Recommendation systems for tourism based on social networks: A survey, arXiv preprint arXiv:1903.12099, 2019"
}
],
"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.i07.pp163-168</referentDoiName>
<primaryReferentType>Creation</primaryReferentType>
<registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
<issueDate>2026-10-04</issueDate>
<issueNumber>1</issueNumber>
<referentCreation>
<name primaryLanguage="en">
<value>A Hybrid Framework For Travel Advice System Using Big Data And AI</value>
<type>PrincipalTitle</type>
</name>
<identifier>
<nonUriValue>10.46243/jst.2023.v8.i07.pp163-168</nonUriValue>
<uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2023.v8.i07.pp163-168</uri>
<type>DOI</type>
</identifier>
<identifier>
<uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/761</uri>
<type>URI</type>
</identifier>
<identifier>
<uri>https://www.jst.org.in/index.php/pub/article/download/761/685</uri>
<type>URI</type>
</identifier>
<identifier>
<uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/761/1742</uri>
<type>URI</type>
</identifier>
<identifier>
<uri>https://jst.org.in/admin/uploads/NUSRATH%20ZEESHAN%20(21S41F0044).pdf</uri>
<type>URI</type>
</identifier>
<structuralType>Digital</structuralType>
<mode>Visual</mode>
<character>Language</character>
<type>JournalArticle</type>
<principalAgent>
<name>
<value>Nusrath Zeeshan Nusrath Zeeshan</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 & Technology</value>
<type>PrincipalTitle</type>
</name>
<identifier>
<nonUriValue>2456-5660</nonUriValue>
<type>ISSN</type>
</identifier>
<referentCreationRole>Part</referentCreationRole>
<referentCreationSequenceIdentifier>
<value>8</value>
<type>VolumeNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>7</value>
<type>IssueNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>163-168</value>
<type>PageNumber</type>
</referentCreationSequenceIdentifier>
</linkedCreation>
<language>en</language>
<languageOfReferentContent>
<language>en</language>
<languageOfReferentContentType>Original</languageOfReferentContentType>
</languageOfReferentContent>
<creationDate>
<date>2023-08-07</date>
<creationDateType>PublicationDate</creationDateType>
</creationDate>
</referentCreation>
</kernelMetadata>
