10.46243/jst.2021.v6.i04.pp25-30 registered
Predicting Air Pollutant using Data Mining and Machine Learning Algorithms
Resolves to https://www.jst.org.in/index.php/pub/article/view/758
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i04.pp25-30
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 f12da5ad6ccf9fdc…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Predicting Air Pollutant using Data Mining and Machine Learning Algorithms (PrincipalTitle)
Published 2021-08-16
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 01 · pages 25–30
Agents
- Isha Jagtap (author)
- Prof. Nandini Babbar (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2021.v6.i04.pp25-30
Abstract
Air pollution can be defined presence of harmful or hazardous substances in the air which deteriorate the quality of air. As we are moving ahead in future the environment is getting polluted day by day due to these biological molecules and harmful gases. These pollutant causes diseases, allergy, etc and death as well. The main aim of this article is to study data mining and machine learning algorithms for predicting air pollutants, especially PM2.5 .So as to control the emission of these harmful substances This is a scientific approach for predicting PM2.5 level in the air using a data set containing different attributes.
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.2021.v6.i04.pp25-30 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Predicting Air Pollutant using Data Mining and Machine Learning Algorithms (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Isha Jagtap author: Prof. Nandini Babbar publisher: Longman Publishers published: 2021-08-16 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 25–30 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 | 2026-09-11 | 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.
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) | 85 fields set · sha256 8653f4547e50… |
| 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.
