Soft Computing Techniques to Predict the Future of Indian Agriculture - Kumar, Surjeet; Kumar Sanyal, Manas
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India, which has the most agricultural tillage area in the world, is one of the massive cultivators of crops. Besides, rice and wheat is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian agriculture production. Here, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and tried to develop a hybrid model to get the optimum result. The vital aspect of this proposed prediction model is to achieve improved accuracy. The Prediction…mehr

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Produktbeschreibung
India, which has the most agricultural tillage area in the world, is one of the massive cultivators of crops. Besides, rice and wheat is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian agriculture production. Here, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and tried to develop a hybrid model to get the optimum result. The vital aspect of this proposed prediction model is to achieve improved accuracy. The Prediction performance has been assessed by using error finding equations like Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Average Error.
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Autorenporträt
B.Tech (Ciencias de la Computación), M.Tech(Ciencias de la Computación), cursando el doctorado, Universidad de KalyaniBengala Occidental, India.