Evidence map›Paper›PMID 38253919›Full record

ArticleVirus genes2024

Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese migration optimization algorithm.

L Ramachandran, S P Mangaiyarkarasi, A Subramanian, S Senthilkumar

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Article in Virus genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.6field-weighted citation impact, top 5% 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

7 citing papers in PubMed, 19 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

L RamachandranDepartment of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, 611002, India. fourstar.lr@gmail.com.
S P MangaiyarkarasiDepartment of Electrical and Electronics Engineering, University College of Engineering, Panruti Campus, Panruti, Tamilnadu, 607106, India.
A SubramanianSrinivasa Ramanujan Centre, SASTRA Deemed to Be University, Kumbakonam, Tamilnadu, 612001, India.
S SenthilkumarDepartment of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, 611002, India.
Open International University for Alternative Medicines · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The major dangerous viral infection for cultivated shrimps is WSSV. The virus is extremely dangerous, spreads swiftly, and may result in up to 100% mortality in 3-10 days. The vast wrapped double stranded DNA virus known as WSSV describes a member of the Nimaviridae viral family's species Whispovirus. It impacts a variety of crustacean hosts but predominantly marine shrimp species that are raised for commercial purposes. The entire age groups are affected by the virus, which leads to widespread mortality. Mesodermal and ectodermal tissues, like the lymph nodes, gills, and cuticular epithelium, represents the centres of infection. Complete genome sequencing related to the WSSV strains from Thailand, China, and Taiwan has identified minute genetic variations amongst them. There exist conflicting findings on the causes of WSSV pathogenicity, which involve variations in the size associated with the genome, the count of tandem repeats, and the availability or lack of certain proteins. Hence, this paper plans to perform the shrimp classification for the WSSV on the basis of novel deep learning methodology. Initially, the data is gathered from the farms as well as internet sources. Next, the pre-processing of the gathered shrimp images is accomplished using the LBP technique. These pre-processed images undergo the segmentation process utilizing the TGVFCMS approach. The extraction of the features from these segmented images is performed by the PLDA technique. In the final step, the classification of the shrimp into healthy shrimp and WSSV affected shrimp is done by the EGRU, in which the parameter tuning is accomplished by the wild GMO algorithm with the consideration of accuracy maximization as the major objective function. Performance indicators for accuracy have been compared with those of various conventional methods, and the results show that the methodology is capable of accurately identifying the shrimp WSSV illness.

Indexed as

PenaeidaeVirus DiseasesWhite spot syndrome virus 1AnimalsEpitheliumGeeseEnhanced gated recurrent unitShrimp classificationWhite spot syndrome detectionWild geese migration optimization algorithm

Identifiers

PMID38253919
OpenAlexW4391099954

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.