Evidence map›Paper›PMID 35765602›Full record

ArticleSoftware impacts2022

Source Code for Optimized Parallel Inception: A Fast COVID-19 Screening Software.

Alireza Tavakolian, Farshid Hajati, Alireza Rezaee, Amirhossein Oliaei Fasakhodi, Shahadat Uddin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Software impacts, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Alireza TavakolianDepartment of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, N Kargar, 1439957131, Tehran, Iran.
Farshid HajatiCollege of Engineering and Science, Victoria University Sydney, 160 Sussex Street, Sydney, NSW 2000, Australia.
Alireza RezaeeDepartment of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, N Kargar, 1439957131, Tehran, Iran.
Amirhossein Oliaei FasakhodiDepartment of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, N Kargar, 1439957131, Tehran, Iran.
Shahadat UddinSchool of Project Management, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. These two viruses have the same symptoms and occur at a collision timeline. Optimized Parallel Inception (OPI) presents a new strategy to screen the COVID-19 from H1N1 with use of only symptoms. In this paper, the process of preprocessing, screening, and specifying feature importance by OPI and particle swarm optimization is presented. Experimental results indicate 98.88 accuracy for screening COVID-19, H1N1, and Neither COVID-19 Nor H1N1.

Indexed as

CoronavirusCOVID-19Deep LearningH1N1 virusOutbreakScreening

Identifiers

PMID35765602
PMCPMC9221174

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.