Classifying five ornamental fish species of Cichlidae family by use of logistic regression and discrimination linear analysis
Publish place: International Journal of Aquatic Research and Environmental Studies، Vol: 1، Issue: 1
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_INJOAR-1-1_002
تاریخ نمایه سازی: 27 خرداد 1402
Abstract:
This experiment was conducted to classify five ornamental fish species Heros severus, Astronotus ocellatus, Pterophyllum scalare, Amphilophus citrinellus× Paraneetroplus melanurus and Amphiprion perideraion regard to the growth indices BWI, SGRW and SGRL by Use of Logistic Regression and Discrimination Linear Analysis, in a completely random design with five treatments (۵ species) each with four replicates with a density of ۲۵ specimens per replicate and totally equal to ۵۰۰ ornamental fish. Feeding trail with Iso-nitrogenous and Iso-caloric diets lasted for ۹۰ days, and every thirty days fish biometry was being practiced. At the end of the trial, data were collected to find the best class for each species regard to the least distances with other species. The results obtained from two statistical models were compared and it was concluded that the Logistic Regression model in species classification based on the type of nutrition and considering the BWI and SGRW indices rather than the describes by Discrimination linear analysis, is more appropriate. But the SGRL index was not considered as an appropriate factor for the separation of fish samples according to species factor in either of these two models.
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Authors
N Mooraki
Department of Fisheries Science, College of Marine Science and Technology, North Tehran Branch, Islamic Azad University (IAU), Tehran, Iran.
M Omrani
Department of Fisheries Science, College of Marine Science and Technology, North Tehran Branch, Islamic Azad University (IAU), Tehran, Iran.
A.E Khajehrahimi
Department of Fisheries Science, College of Marine Science and Technology, North Tehran Branch, Islamic Azad University (IAU), Tehran, Iran.
P Azhdari
Department of Statistics, College of Science, North Tehran Branch, Islamic Azad University (IAU), Tehran, Iran.