RETRACTED: Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment (Retracted Article)

被引:0
作者
Kong, Sibo [1 ,2 ]
机构
[1] Chongqing Coll Mobile Commun, Chongqing 401520, Peoples R China
[2] Chongqing Key Lab Publ Big Data Secur Technol, Chongqing 401420, Peoples R China
关键词
FORECAST;
D O I
10.1155/2022/1132270
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
From the environmental and security status quo faced by big data, although big data is unstructured or more difficult to filter and analyze, it does not mean that big data is necessarily more secure. In order to be invincible in the fierce market competition, an enterprise must conduct an in-depth understanding and investigation of the rapid changes in the market and economic development.)e current era is already the Internet era.)e arrival of the era of network communications has greatly facilitated people's lives. However, how to seize the opportunities in the era of network communication and make economic predictions based on actual situations are particularly important for enterprises.)erefore, we are required to make reasonable economic predictions of network communication services in order to seize opportunities and meet challenges. By analyzing the development and application of artificial neural network (NN), this article briefly introduces its development and principles. Based on the reality of a network communication industrial enterprise, it uses data modeling and comparative analysis to establish a logistic regression model, a decision tree model, and a BP NN.)e model compares the customer types and data predictions of the enterprise's data traffic under the three models. From the results of the model analysis, it can be seen that in the analysis of the three models, the ROC curve analysis, the BPNNpredicts that the cumulative hit rate in the ROC curve is wider. More users who can handle traffic services will be covered. In the cumulative customer lift analysis, under the first set of data (that is, when the depth is 20), the cumulative depths of the decision tree model, logic analysis model, and BP neural system prediction model are 2.5, 3.4, and 3.6, respectively.)e BP neural system prediction model has the highest value of cumulative depth. In the cumulative capture response percentage analysis, the cumulative capture response percentage from low to low is the decision tree model and logic analysis. From BP NN prediction model, we can draw the conclusions.)e network system can play a good role in forecasting and help enterprise managers make economic decisions.
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页数:9
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