Evidence map›Paper›PMID 35669983›Full record

ArticleInternational journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management2022

An IoT/FOG based framework for sports talent identification in COVID-19 like situations.

Naveed Jeelani Khan, Gulfam Ahamad, Mohd Naseem

Open access · bronzeAbstract read
In one paragraph

Article in International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management, 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
2.0field-weighted citation impact, top 15% of its field
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, 11 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Naveed Jeelani KhanDepartment of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir 185234 India.ORCID 0000-0003-3574-8708
Gulfam AhamadDepartment of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir 185234 India.
Mohd NaseemDepartment of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir 185234 India.
Baba Ghulam Shah Badshah University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 crippled all the domains of our society. The inevitable lockdowns and social distancing procedures have hit the process of traditional sports talent identification (TiD) severely. This will interrupt the career-excellency of athletes and will also affect the future talent in the years to come. We explore the effect of COVID-19 on sports talent identification and propose an IoT/Fog-based framework for theTiD process during COVID-19 and COVID-like situations. Our proposed novel six-layer model facilitates the sports talent identification remotely using the various latest Information and Communication Technologies like IoT, fog and cloud computing. All the stakeholders like experts, coaches, players, institutes etc. are taken into consideration. The framework is mobile, widely accessible, scalable, cost-effective, secure, platform/location independent and fast. A brief case study of cricket talent identification using the proposed framework is also provided.

Indexed as

Cloud computingCOVID-19 and sportsCricket talent identificationFog computingInternet of things (IoT)Sports talent identificationTechnology for sports in COVID-19 times

Identifiers

PMID35669983
PMCPMC9148553
OpenAlexW4281647597

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.