10.46243/jst.2021.v6.i04.pp345-351 registered
Covid-19 Prediction Using Deep Convolutional Neural Networks
Resolves to https://www.jst.org.in/index.php/pub/article/view/714
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i04.pp345-351
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 6cc3b6ba45aefeed…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Covid-19 Prediction Using Deep Convolutional Neural Networks (PrincipalTitle)
Published 2021-08-16
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 01 · pages 345–351
Agents
- Shantanu S. Badve (author)
- Dhanraj M. Tapase Tapase (author)
- Abhishek S. Bangale (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2021.v6.i04.pp345-351
Abstract
The currently available methods such as RT-PCR for the detection of the novel coronavirus disease fail due to restricted supply of test kits and few favourable signs of illness in the early phases, requiring the use of alternative solutions. The use of an Artificial Intelligence (AI) tool, could assist the world in developing an additional disease prevention regulation. An automatic detection technique is provided in this method, which leverages information from Computer Tomography (CT) images to train the deep learning model CNN architecture. CNNs are the best deep learning model option due to its promising accuracy for biomedical images and availability of fewer samples, which satisfies the need for CNN training. The presented paper aims to discuss the various aspects of the system, beginning with a brief summary and gradually progressing to explain the various implementations, which include the datasets used, the use of the State of the Art (SOTA), and a discussion of the various metrics used for evaluation. Finally, a user-interactive system is presented that employs the qualified model in the area.
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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.pp345-351 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Covid-19 Prediction Using Deep Convolutional Neural Networks (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Shantanu S. Badve author: Dhanraj M. Tapase Tapase author: Abhishek S. Bangale publisher: Longman Publishers published: 2021-08-16 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 345–351 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.
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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) | 101 fields set · sha256 41244b0213f7… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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