Evidence map›Paper›PMID 35974898›Full record

ArticleSmart health (Amsterdam, Netherlands)2022

An intelligent spatial stream processing framework for digital forensics amid the COVID-19 outbreak.

Sujit Bebortta, Aditya Ranjan Dalabehera, Bibudhendu Pati, Chhabi Rani Panigrahi, Gyana Ranjan Nanda, Biswajit Sahu, Dilip Senapati

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Smart health (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sujit BeborttaDepartment of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.
Aditya Ranjan DalabeheraDepartment of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.
Bibudhendu PatiDepartment of Computer Science, Rama Devi Women's University, Bhubaneswar, 751022, Odisha, India.
Chhabi Rani PanigrahiDepartment of Computer Science, Rama Devi Women's University, Bhubaneswar, 751022, Odisha, India.
Gyana Ranjan NandaDepartment of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.
Biswajit SahuDepartment of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.
Dilip SenapatiDepartment of Computer Science, Ravenshaw University, Cuttack, 753003, Odisha, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent times, several strategies to minimize the spread of 2019 novel coronavirus disease (COVID-19) have been adopted. Some recent technological breakthroughs like the drone-based tracking systems have focused on devising specific dynamical approaches for administering public mobility and providing early detection of symptomatic patients. In this paper, a smart real-time image processing framework converged with a non-contact thermal temperature screening module was implemented. The proposed framework comprised of three modules

Indexed as

AutocorrelationCOVID-19Event triggered video framing (ETVF)Poisson processQueuing theoryReal-time video framing (RTVF)

Identifiers

PMID35974898
PMCPMC9373597

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.