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Fish classification using extraction of appropriate feature set

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 3 (2022)

DOI
10.11591/ijece.v12i3.pp2488-2500
ISSN
2722-2578, 2088-8708
Published
2022-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%.

Collaboration network of the authors

30 authors · 196 co-authorship links · 56 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Imam Abdulrahman Bin Faisal University
  • Jerash University
  • Amman Arab University
  • Gulf Medical University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mutasem K. AlsmadiImam Abdulrahman Bin Faisal University6
Hayat AlfaghamImam Abdulrahman Bin Faisal University4
Ibrahim AlmarashdehImam Abdulrahman Bin Faisal University3
Jehad Saad AlqurniImam Abdulrahman Bin Faisal University3
Ghaith M. JaradatAmman Arab University3
Mohammed Fahed TayfourImam Abdulrahman Bin Faisal University2
Raed Ali AlkhasawnehImam Abdulrahman Bin Faisal University2
Firas HaddadImam Abdulrahman Bin Faisal University2
Malek AlzaqebahImam Abdulrahman Bin Faisal University2
Sana JawarnehImam Abdulrahman Bin Faisal University2
Fahad AlGhamdiImam Abdulrahman Bin Faisal University2
Tamer FaragImam Abdulrahman Bin Faisal University1
Walaa HassanImam Abdulrahman Bin Faisal University1
Eman A. E. AhmedImam Abdulrahman Bin Faisal University1
Lubna Mahmoud Abdel JawadGulf Medical University1
Muneerah AlshabanahImam Abdulrahman Bin Faisal University1
Daniah AlrajhiImam Abdulrahman Bin Faisal University1
Hanouf AlkhaldiImam Abdulrahman Bin Faisal University1
Sami A. AbahussainImam Abdulrahman Bin Faisal University1
Njoud AltuwaijriImam Abdulrahman Bin Faisal University1
Hany Answer ShoShanImam Abdulrahman Bin Faisal University1
Hayah Mohamed AbouelnagaImam Abdulrahman Bin Faisal University1
Ahmed Baz Mohamed MetwallyAffiliation unknown1
Mahmoud Al-ShugranJerash University1
Ahmad Abu-Al-AishJerash University1
Majd AL HawamdehJerash University1
Mutaz Falah GharaibehImam Abdulrahman Bin Faisal University1
Tariq AlsallakAmman Arab University1
Latifa Abdullah AlmusfarImam Abdulrahman Bin Faisal University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Fish classification using extraction of appropriate feature set</dc:title><dc:creator>Badawi, Usama A.</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i3.pp2488-2500</dc:identifier><dc:identifier>10.11591/ijece.v12i3.pp2488-2500</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 3 (2022); 2488</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/23755/15702</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>