Neural Network and Data Mining Method in Aerodynamic Optimization

2011 ◽  
Vol 52-54 ◽  
pp. 1421-1426
Author(s):  
Xin Jin ◽  
Gang Sun ◽  
Chun Juan Liu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Qualitative Analysis ◽  
Optimization Process ◽  
Mining Method ◽  
Aerodynamic Optimization ◽  
Mining Technology ◽  
Quantitative Calculation ◽  
Using Data ◽  
Intelligent Approach

This document focuses on the case that optimization direction is hard to determine in aerodynamic optimization processes, and proposes an intelligent approach based on ANN learning, followed by result visualization using data mining technology. It can not only help visualize the optimization process (qualitative analysis), but also determine the optimization direction (quantitative calculation), which can save time of frequent CFD calculation and obviously improve efficiency in aerodynamic optimization.

The Prediction of River Water Pollution Density Based on Data Mining Technology

2010 ◽  
Vol 113-116 ◽  
pp. 1285-1288
Author(s):  
Chen Ye Wang ◽  
Bin Sheng Liu ◽  
Er Wei Qiu
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Rough Set Theory ◽  
Mining Technology ◽  
Bp Artificial Neural Network ◽  
Three Stages ◽  
Artificial Neural ◽  
Using Data

In order to increase the prediction precision, this article proposes a forecasting model in water pollution density based on data mining technology. The model consists of three stages: first, the rough set theory and the genetic algorithm are applied to select relevant forecasting variable to the water pollution density; second, training pattern of artificial neural network which is similar to the forecast term is carried out by using data mining technology; finally the artificial neural network is used to carry on forecasting the water pollution density. The applied result shows that this model has a higher precision and surpasses gray GM (1, 1) and the pure BP artificial neural network model.

scholarly journals Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network

2021 ◽  
Vol 4 (2) ◽  
pp. 205-216
Author(s):  
Amri Muliawan Nur ◽  
◽  
Imam Fathurrahman ◽  
Yahya Yahya ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Processing ◽  
Statistical Data ◽  
Network Data ◽  
Mining Method ◽  
The Neural Network ◽  
Using Data ◽  
Data Processing Methods

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976

Predicting customer churn in mobile industry using data mining technology

2017 ◽  
Vol 117 (1) ◽  
pp. 90-109 ◽  
Author(s):  
Eui-Bang Lee ◽  
Jinwha Kim ◽  
Sang-Gun Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Online News ◽  
Partial Least Square ◽  
Churn Prediction ◽  
Mining Technology ◽  
Content Type ◽  
Customer Churn ◽  
Mobile Industry ◽  
Using Data

Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

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