Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)–LR algorithms




چکیده: (953 مشاهده) 
In this study, morphometric features such as total length, standard length, and fork length obtained from a total of 321 Sparus aurata samples, including 164 females and 157 males, captured between 2012 and 2013 from İskenderun Bay were used as input value, while weight was used as an output value. The Artificial Neural Network (MLPMultiL Layer Perceptron) as well as the M5P algorithm and Linear Regression (LR) algorithm from version 3.7.11 of the WEKA Program were applied. When coefficients of correlation were assessed, the MLP algorithm for males, females and the total were calculated as 0.9686, 0.9605 and 0.9663, respectively; the M5P algorithm for males, females and the total were calculated as 0.9722, 0.9596 and 0.9735, respectively; and the LR Model for males, females and the total were calculated as 0.9777, 0.9498 and 0.9473, respectively. With respect to the Mean Absolute Error (MAE) calculations, the MLP algorithm MAE values for males, females and the total were calculated as 2.94, 2.57 and 2.7074, respectively; the M5P algorithm MAE values for males, females and the total were calculated as 2.400, 2.641 and 2.157, respectively; and the LR Model MAE values for males, females and the total were calculated as 3.217, 2.811 and 3.11, respectively. It can also be concluded from the study that, in order to predict ANN interactions Nonlinear Regression model is more effective and has better performance than the conventional models. 



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نوع مطالعه: پژوهشي 
موضوع مقاله:
Stock assessment دریافت: 1395/12/6  پذیرش: 1396/5/9  انتشار: 1399/1/1





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SANGÜN L, Güney O, Kokcu P, Basusta N. Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)–LR algorithms. IJFS. 2020; 19 (2) :541550 URL: http://jifro.ir/article12611fa.html
Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)–LR algorithms. مجله علوم شیلاتی ایران. 1398; 19 (2) :541550 URL: http://jifro.ir/article12611fa.html
