Evidence map›Paper›PMID 35645427›Full record

ArticleComputers & electrical engineering : an international journal2022

Autonomous service for managing real time notification in detection of COVID-19 virus.

Yousef Methkal Abd Algani, K Boopalan, G Elangovan, D Teja Santosh, K Chanthirasekaran, Indrajit Patra, N Pughazendi, B Kiranbala, R Nikitha, M Saranya

RetractedOpen access · greenAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers & electrical engineering : an international journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Yousef Methkal Abd AlganiDepartment of Mathematics, Sakhnin College, Israel.
K BoopalanAnnamacharya Institute of Technology and Sciences, Rajampet, India.
G ElangovanDepartment of Artificial Intelligence, Shri Vishnu Engineering College for Women, India.
D Teja SantoshComputer Science Engineering, CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam, T.S.-501 510, India.
K ChanthirasekaranDepartment of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India.
Indrajit PatraNIT Durgapur, West Bengal, India.
N PughazendiDepartment of CSE, Panimalar Engineering, College, Tamil Nadu, India.
B KiranbalaDepartment of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering, Trichy, Tamil Nadu, India.
R NikithaDepartment of Information Technology, RMD Engineering College, Chennai, India.
M SaranyaDepartment of CSE, R M K College of Engineering and Technology, Chennai, India.
G.B. Pant Institute of Himalayan Environment and Development · INKPR Institute of Engineering and Technology · INNational Institute of Technology Durgapur · INNorth Eastern Regional Institute of Science and Technology · INSakhnin College · ILSaveetha University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In today's world, the most prominent public issue in the field of medicine is the rapid spread of viral sickness. The seriousness of the disease lies in its fast spreading nature. The main aim of the study is the proposal of a framework for the earlier detection and forecasting of the COVID-19 virus infection amongst the people to avoid the spread of the disease across the world by undertaking the precautionary measures. According to this framework, there are four stages for the proposed work. This includes the collection of necessary data followed by the classification of the collected information which is then taken in the process of mining and extraction and eventually ending with the process of decision modelling. Since the frequency of the infection is very often a prescient one, the probabilistic examination is measured as a degree of membership characterised by the fever measure related to the same. The predictions are thereby realised using the temporal RNN. The model finally provides effective outcomes in the efficiency of classification, reliability, the prediction viability etc.

Indexed as

Cloud computingCOVID-19, Fog computingInternet of ThingsTemporal RNN, Virus

Identifiers

PMID35645427
PMCPMC9130642
OpenAlexW4281558465

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.