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- W3136831837 abstract "In the real world, it is very difficult for fish farmers to select the perfect fish species for aquaculture in a specific aquatic environment. The main goal of this research is to build a machine learning that can predict the perfect fish species in an aquatic environment. In this paper, we have utilized a model using random forest (RF). To validate the model, we have used a dataset of aquatic environment for 11 different fishes. To predict the fish species, we utilized the different characteristics of aquatic environment including pH, temperature, and turbidity. As a performance metrics, we measured accuracy, true positive (TP) rate, and kappa statistics. Experimental results demonstrate that the proposed RF-based prediction model shows accuracy 88.48%, kappa statistic 87.11% and TP rate 88.5% for the tested dataset. In addition, we compare the proposed model with the state-of-art models J48, RF, k-nearest neighbor (k-NN), and classification and regression trees (CART). The proposed model outperforms than the existing models by exhibiting the higher accuracy score, TP rate and kappa statistics." @default.
- W3136831837 created "2021-03-29" @default.
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- W3136831837 date "2021-09-01" @default.
- W3136831837 modified "2023-09-27" @default.
- W3136831837 title "Fish survival prediction in an aquatic environment using random forest model" @default.
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- W3136831837 doi "https://doi.org/10.11591/ijai.v10.i3.pp614-622" @default.
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