10.46243/jst.2022.v7.i01.pp188-198 registered
EFFICIENT MACHINE LEARNING MODEL TO IDENTIFY THE LUNG CANCER USING DYNAMIC FEATURE EXTARCTION
Resolves to https://www.jst.org.in/index.php/pub/article/view/349
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2022.v7.i01.pp188-198
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 0f855994813d31ae…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
EFFICIENT MACHINE LEARNING MODEL TO IDENTIFY THE LUNG CANCER USING DYNAMIC FEATURE EXTARCTION (PrincipalTitle)
Published 2023-07-26
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 7 · issue 1 · pages 188–198
Agents
- ASIYA ASIYA (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2022.v7.i01.pp188-198
Abstract
An estimated 1.2 million people were diagnosed with lung cancer in 2000, making it the most frequent disease worldwide (12.3% of all malignancies). Cigarette smokers are responsible for 80% to 90% of lung cancers. In both sexes, lung cancer continues to be the major cause of cancer-related death in the United States and elsewhere. Tobacco use and smoking are responsible for nearly all occurrences of lung cancer. Other causes of lung cancer include exposure to radon gas, asbestos, air pollution, and persistent infections. Further, many potential risk factors for developing lung cancer have been proposed, including both genetic and environmental factors. Small-cell lung carcinomas (SCLC) and non-small-cell lung carcinomas (NSCLC) are the two main histologic subtypes of lung cancer and exhibit distinct patterns of growth and metastasis (NSCLC). Surgery, radiation treatment, chemotherapy, and targeted therapy are all viable alternatives for treating lung cancer. Different characteristics, such as the nature and extent of the malignancy, inform suggestions for treatment approaches. A diagnosis of lung cancer at an early stage can save the lives of patients. Several machine learning algorithms were used to make lung cancer forecasts in this study.
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.2022.v7.i01.pp188-198 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | EFFICIENT MACHINE LEARNING MODEL TO IDENTIFY THE LUNG CANCER USING DYNAMIC FEATURE EXTARCTION (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: ASIYA ASIYA publisher: Longman Publishers published: 2023-07-26 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 1 · pp. 188–198 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 | 2024-02-16 | 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.
Recommended for a journal article and not in this record: the reference list.
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) | 52 fields set · sha256 4d77af6b4d9e… |
| 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.
