Postulating Support Vector Regression Model to Measure the Effect of Protein, Carbohydrate and Fats on the Weight of Carb Fish

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Rawa saman maaroof, Sham azad Rahim, Shahla Othman salih, Hindreen Abdullah Taher

Abstract

In this paper we studied the combination among the levels of carbohydrate (20%, 30%, 40% and 50%),  protein (8%, 12%, 16% and 20%) and fats (5%, 10%, 15% and 20%), where all possible combinations are 64. We gave each combination of the aforementioned elements to an aquarium fish with volume of 1.92 m3, each aquarium contained 5 fishes, the aim of our study is to detect which combination the three elements are record a high weight of the fishes, here we depend on the average of the fishes weight and the results clarified that the combination (15%, 12% and 10%) of 1kg for carbohydrate, protein and fats respectively are gave average weight of 2.312 kg, for this purpose SVR has been used. According to the results radial kernel function gave highest performance compared to the other kernel functions, the R2 = 89% this implies the factors capable of explaining 92% of fishes weight with MSE and RMSE of (0.000506 and 0.02249) respectively. And p-values of the three aforementioned variables are  less than the significant level of 0.01, implying that the three factors have a statistically significant impact on the fishes weight. Where carbohydrate has an impact of 0.0021 on the fishes weight, in another word if carbohydrate increase by 1% unite, then the weight of fishes increase by 0.002 grams, also both protein and fats have a significant positive effect on the response variable, and the amount of impacts are (0.0136 and 0.0014) respectively.

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