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Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture

Publication Type : Journal Article

Publisher : Elsevier

Source : Elsevier

Url : https://www.sciencedirect.com/science/article/abs/pii/S0045790620305735?casa_token=Rn00TR-GjQIAAAAA:cTyG0bGcLpLRhskQckq6c5PSCQ_jOG_HwnkQabO23sad0MBudHOP_oeBGJ2vdN8YoLBio8Mn

Campus : Amritapuri

School : School of Computing

Department : Computer Science

Year : 2020

Abstract : The Neural Network has a significant impact in developing predictive models in a wide range of applications. In this paper, a neuro-fuzzy prediction model is developed depending on improving the performance of the traditional artificial neural networks using Adaptive Momentum Optimizer. This optimizer simulates the behavior of the International Trade Analysis in the agriculture industry, and this method is used to determine the optimal parameters of artificial neural networks . The proposed model is compared with the existing models such as Support Vector Machine , Random Forest , Decision Tree and traditional Artificial Neural Network models . To examine the forecasting performance of the proposed approach, agriculture datasets is used. The performance of the models was assessed using different performance evaluation criteria and the empirical results show that the back propagation neural network with Adam optimizer attains favorable prediction accuracy of 96.78%, and a better convergence rate. Compared to other benchmark algorithms, the proposed algorithm performs better, and the result validates the effectiveness of the back propagation with Adam optimizer for Natural Language Processing .

Cite this Research Publication : Remya S, Sasikala R. Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture. Computers & Electrical Engineering. 2020 Sep 1;86:106718 .

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