scholarly journals Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaojun Jiang
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Warning System ◽  
College Teaching ◽  
Information Value ◽  
Retention Prediction ◽  
Minimum Problem ◽  
Mining Technology ◽  
Teaching Management ◽  
Advance Student

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.

scholarly journals Use of Data Mining Techniques to Detect Medical Fraud in Health Insurance

2013 ◽  
Vol 284-287 ◽  
pp. 1574-1578 ◽  
Author(s):  
Kuo Chung Lin ◽  
Ching Long Yeh ◽  
Shih Ying Huang
Keyword(s):  
Data Mining ◽  
Health Insurance ◽  
Semantic Network ◽  
Warning System ◽  
Mining Technology ◽  
Periodic Data ◽  
The Future ◽  
Accuracy Ratio ◽  
Database Technology ◽  
Set Up

The medical health insurance claims application case the inspection usually relies on experts’ experience for verification and experienced personnel in charge for checking. This paper takes advantage of data-mining technology to design models and find out cases requiring for manual inspection so as to save time and manpower. By the analysis of the 20/80 principle and the coverage and accuracy ratio, a great number of periodic data (over 2 million records) are fed back to the data-mining models after repetitive verification. Also, it is discovered that to integrate the data-mining technology and feed back to different business stages so as to establish early warning system will be an important topic for the health insurance system in hospital’s EMR in the future. Meanwhile, as the information acquired by data-mining needs to be stored and the traditional database technology has limitations. Next time, this paper explores the ontology framework to be set up by semantic network technology in the future in order to assist the storage of knowledge gained by data-mining.

Research on Employment Data Mining for Higher Vocational Graduates

2014 ◽  
Vol 686 ◽  
pp. 290-294
Author(s):  
Feng Lin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
College Teaching ◽  
Original Data ◽  
Management Decision ◽  
Rule Mining ◽  
Mining System ◽  
Teaching Management ◽  
Effective Use ◽  
Employment Data

In order to make effective use a large amount of graduate data in colleges and universities that accumulate by teaching management of work, the paper study the data mining for higher vocational graduates database using the data mining technology. Using a variety of data preprocessing methods for the original data, and the paper put forward to mining algorithm based on commonly association rule Apriori algorithm, then according to the actual needs of the design and implementation of association rule mining system, has been beneficial to the employment guidance of college teaching management decision and graduates of the mining results.

scholarly journals Application of Data Mining in an Intelligent Early Warning System for Rock Bursts

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 55 ◽  
Author(s):  
Xuejun Zhu ◽  
Xiaona Jin ◽  
Dongdong Jia ◽  
Naiwei Sun ◽  
Pu Wang
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Rock Burst ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Monitoring Data ◽  
Efficient Production ◽  
Mining Technology ◽  
Rock Bursts

In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real-time queries and the analysis of monitoring data through browsers, which is very convenient. Hence, it can make important contributions to the safe and efficient production of coal mines and the sustainable development of the coal economy.

scholarly journals Bed Permeability State Prediction Model of Sintering Process Based on Data Mining Technology

2016 ◽  
Vol 56 (12) ◽  
pp. 2113-2117 ◽  
Author(s):  
Xiaoxian Huang ◽  
Xiaohui Fan ◽  
Xuling Chen ◽  
Guiming Yang ◽  
Min Gan
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Sintering Process ◽  
Mining Technology ◽  
State Prediction ◽  
Permeability State

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

2014 ◽  
Vol 623 ◽  
pp. 229-233 ◽  
Author(s):  
De Jiang Qi ◽  
Hai Yan Hu
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Warning System ◽  
Colleges And Universities ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
Financial Management System ◽  
Analysis System

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

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