01739nas a2200265   4500000000100000000000100001008004100002260001200043653002800055653001900083653002700102653002300129653001500152653001600167100001700183700002400200700002900224700002000253245014600273856009900419300001000518490000600528520092500534022001401459       2019                            d  c09/201910aArtificial Intelligence10aNeural Network10aGray Wolf Optimization10aCultural Algorithm10aRegression10aTime Series1 aAlireza Goli1 aHassan Khademi Zare1 aReza Tavakkoli-Moghaddam1 aAhmad Sadeghieh00aAn Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/03/ijimai20195_6_2_pdf_10531.pdf  a15-220 v53 aThis paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved.  a1989-1660