Open AccessApplication on Development of Modern Service Industry in China Based on Artificial Intelligent Model Optimized by TANSAFOA
Author(s) -
Tian Wang,
Jianbang Lin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012070
Subject(s) - multivariate adaptive regression splines , artificial neural network , computer science , artificial intelligence , lift (data mining) , construct (python library) , service (business) , big data , industrial engineering , data mining , machine learning , engineering , bayesian multivariate linear regression , regression analysis , economy , economics , programming language
Modern service industry is one of the industries that widely applies artificial intelligent (AI) in the era of big data. The development of modern service industry plays a very important role in industrial transformation. The emergence of AI technology improves the effectiveness of data mining, and more and more scholars suggest a series of optimization methods to lift the prediction ability. In this study, tangent fruit fly optimization algorithm with step adjust (TANSAFOA) is proposed and it is used to optimize multivariate adaptive regression splines (MARS) and back propagation neural network (BPNN) to construct a prediction model of business performance. The result shows TANSAFOA can effectively optimize the prediction model and the BPNN model optimized by TANSAFOA has higher prediction performance than MARS. TANSAFOA BPNN-model is the most appropriate prediction model for modern service industry in China.