scholarly journals The Evaluation Model of Legal Risk of Enterprises’ Foreign Investment Based on Data Mining

2021 ◽  
Vol 1744 (3) ◽  
pp. 032175
Author(s):  
Luyuan Yang
Keyword(s):  
Data Mining ◽  
Foreign Investment ◽  
Evaluation Model ◽  
Legal Risk

Data mining artificial intelligence technology for college english test framework and performance analysis system

2020 ◽  
pp. 1-11
Author(s):  
Lin Shen
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Evaluation Model ◽  
Diagnostic Evaluation ◽  
Test Paper ◽  
Test Question ◽  
College English ◽  
Question Recommendation ◽  
And Performance ◽  
Analysis System

This article first studies and designs the college English test framework and performance analysis system. The author analyzes a large number of data collected by the system in three dimensions: using data mining title association models, using machine learning to merge college English score prediction models, and finally diagnosing on the basis of the sexual evaluation model, the author designed and implemented a test paper algorithm based on the association rules of the question type, and carried out relevant verification from the three aspects of test paper time, test question recommendation and improvement according to scores. Finally, according to the needs analysis, the author uses the diagnostic evaluation model and related test paper algorithm to design and implement the diagnostic evaluation model, which is added to the college English diagnostic practice system. It can be obtained through comparative experiments that the paper-based algorithm based on the diagnostic evaluation model proposed in this paper can effectively give better practice guidance and test question recommendation to the learner’s learning status and knowledge point problem obstacles, and can effectively improve learning. The achievements of the authors have broad application prospects and research value.

Appling Data Mining Technology to Analasis Clearance Efficiency in the Port Logistics

2011 ◽  
Vol 480-481 ◽  
pp. 1144-1149
Author(s):  
Ya Qin Wang ◽  
Yu Ming Song
Keyword(s):  
Data Mining ◽  
Foreign Investment ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Positive Impact ◽  
Classification Model ◽  
Mining Method ◽  
Mining Technology ◽  
Scarce Resources ◽  
Port Logistics

Currently, monitoring customs declaration with limited examination of imported goods by available scarce resources poses considerable challenge to the customs authority worldwide. This a positive impact on international trade and foreign investment of a country to find out the limited factors of customs clearance, put forward the improved solution and enhance the efficiency of customs clearance efficiency of port logistics. This paper presents a classification model, which is a sort of data mining technology, to analyze the risk of commodity through customs clearance, and builds a classifier of customs inspection as the reference for customs inspection and monitoring. And the classification model based on BP neural network is established and evaluated through experiments, which are proved that a classification data mining method can be used for risk evaluation on customs clearance business to improve the customs inspection and monitoring.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

2020 ◽  
pp. 1-12
Author(s):  
Zheng Rong ◽  
Zheng Gang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
College Students ◽  
Data Mining ◽  
University Students ◽  
Evaluation System ◽  
Evaluation Model ◽  
Ideological And Political Education ◽  
Political Education ◽  
Ideological Education

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.

Research on ideological and political evaluation model of university students based on data mining artificial intelligence technology

2020 ◽  
pp. 1-10
Author(s):  
Yamei Yin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Political Theory ◽  
Evaluation Model ◽  
Model Performance ◽  
Teaching Evaluation ◽  
Machine Learning Algorithms ◽  
Evaluation Index System ◽  
Algorithm Optimization ◽  
Political Evaluation

The teaching evaluation index system based on artificial intelligence not only evaluates and reflects the teaching situation of ideological and political theory courses in universities as a whole, but also provides specific feasible goals and direction guidance for the construction of ideological and political theory courses in universities. Based on data mining technology, this paper combines machine learning algorithms and dimensional analysis to study the ideological and political evaluation model of colleges and universities and builds an artificial intelligence teaching evaluation model based on actual needs. Moreover, this study transforms the model selection problem into a hybrid optimization algorithm optimization problem, and the algorithm attempts to find the optimal model from the model set. In addition, this study designs a control experiment to perform model performance analysis. The results of the study show that the performance of the model meets the expected goals and can be applied to practice.

Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hui Yu
Keyword(s):  
Data Mining ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Practical Significance ◽  
Expression Recognition

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.

scholarly journals Research on Personnel Performance Evaluation Model Based on Neural Wireless Network Data Mining Algorithm

2020 ◽  
Author(s):  
Wei Liang ◽  
Tingyi Li
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Evaluation Model ◽  
Multiple Input Multiple Output ◽  
Network Data ◽  
Data Mining Algorithm ◽  
Spatial Network ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Frequency Feature

Abstract In order to effectively evaluate personnel performance, a distributed data mining algorithm for spatial networks based on BP neural wireless network is proposed. In the cloud computing environment, an excavator is used to construct multiple input multiple output spatial network data, analyze the data structure, and perform redundant data compression of massive data through time-frequency feature extraction. Combined with adaptive matching filtering method, the characteristics of the data are matched. The spatial frequency feature extraction method is used to locate the features of the multiple-input multiple-output spatial network data, and the fourth-order cumulant slice is used for reorganization. Data in time series. In order to improve the accuracy of data mining, the BP neural network is used to classify and identify the extracted data features to achieve the optimization of data mining. This algorithm improves the accuracy of personnel performance evaluation, and simultaneously establishes a hierarchical analysis and quantitative evaluation model for the performance of government managers, and adjusts the results of hierarchical statistical analysis on government administrators as needed. The performance evaluation and optimization of government administrators were introduced. The empirical analysis results show that the method has higher accuracy for government managers' performance evaluation, higher efficiency of big data processing and better integration.

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