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Automatic Evaluation Model of Physical Education Based on association rules algorithm

WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 2797-2805页

作者： Zhang, Tong Zhongnan Univ Econ & Law Dept Phys Educ Wuhan Hubei Peoples R China

As physical education rapidly develops in China currently, various technology, especially the computer technology is also used in the physical education. Automatic evaluation model of physical education based on association rules algorithm was studied in the paper. association rule recommendation algorithm was firstly expounded in this paper. Then, the indexes of automatic evaluation model of physical education based on the collaborative filtering algorithms of association rule were established. Through the real teaching data of one university, the algorithm established in the paper was tested, and the process of physical education in colleges and universities was evaluated. Finally, it was concluded that the algorithm could effectively describe and evaluate the behavior of the physical education teaching and had the potential for popularization.

关键词： association rules algorithm Physical education Automatic evaluation

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Critical Causal Path Analysis of Subway Construction Safety Accidents Based on Text Mining

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2025年第1期11卷

作者： Huo, Xiaosen Du, Shuang Jiao, Liudan Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China

Due to the frequent occurrence of subway construction safety accidents in recent years, construction workers face great safety threat, which produces serious social hazards. The causes of subway construction safety accidents are complex and require systematic causality analysis. Therefore, this study conducts a data-driven exploration, 209 subway construction safety accident investigation reports from 2005 to 2022 are collected as the original data, and the causal paths and intrinsic mechanisms of subway construction safety accidents are analyzed, aiming to improve the safety management ability and work safety in subway construction. Firstly, a total of 116 key features associated with subway accidents are extracted from the original data by text mining, which are utilized to develop an improved Human Factors Analysis and Classification System model (HFACS). Subsequently, a novel critical causal path screening model is proposed, by combining the Apriori algorithm in the association rules algorithm with gray relational analysis. Finally, the identified critical causal paths are verified using the Jaccard similarity calculation method, which demonstrates a high degree of similarity with the original accident data and provides empirical evidence of the effectiveness and rationality of the proposed model. This study provides a new perspective for exploring the causative factors of subway construction safety accidents and the complex interaction mechanisms. Cutting off the causal propagation in the accident causal paths can eliminate potential accidents and improve the safety of subway construction.

关键词： Subway construction safety accidents Text mining HFACS model association rules algorithm Accident causal path

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A Weighted association rules Mining algorithm with Fuzzy Quantitative Constraints

A Weighted Association Rules Mining Algorithm with Fuzzy Qua...

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International Conference on Information Science and Cloud Computing Companion (ISCC-C)

作者： Lu, Qibing Sheng, Buyun Wuhan Univ Technol Sch Mech Engn Hubei Digital Mfg Key Lab Wuhan 430000 Peoples R China

ISBN: (纸本)9781479952458

Along with production process automation and development of new products, manufacturing information in large quantity, contains more dimensions, in order to mine useful information from the manufacturing database, monitor and control manufacturing process effectively. A weighted association rules mining algorithm with fuzzy quantitative constraints (FQC-wed Apriori algorithm) is proposed in this paper. First, find association rules after database mining. Then, mine fuzzy association rules with fuzzy query. Last, find frequent itemsets with the improved weighted association rules algorithm. Manufacturing process information can be mined and effectiveness of the mining algorithm can be evaluated. The algorithm is applied to manufacturing process information mining in discrete manufacturing industry.

关键词： data mining association rules algorithm weighted support fuzzy association

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Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

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IEEE ACCESS 2020年 8卷 84455-84466页

作者： Zhang, Jingxuan Sun, Hexu Sun, Zexian Dong, Weichao Dong, Yan Gong, Siyuan Hebei Univ Technol Sch Artificial Intelligence Tianjin 300130 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China Hebei Construct & Investment Grp Co Ltd Dept Sci & Informat Technol Shijiazhuang 050000 Hebei Peoples R China

In order to cooperate the wind farm operators with grasping the operation status of wind power converter, a novel reliability assessment strategy is proposed based on supervisory control and data acquisition (SCADA) multistate parameters prediction of permanent magnet synchronous generator (PMSG) wind turbine. The strategy considers "off-line training, on-line matching and assessment". The operation reliability of wind power converter is obtained via the analysis and weight computing of confidence level, prediction value and actual value of SCADA multistate parameters. In the "off-line training" part, first, the FP-Growth association algorithm is employed to analyze the confidence levels of SCADA variables to the faults of wind power converter. The variables with high confidence level are defined as SCADA multistate parameters. Afterwards, wavelet packet transform (WPT) and K-means algorithm are employed to decompose, reconstruct, normalize and cluster the time series (off-line data) of multistate parameters under normal operation of wind turbine, to improve the generalization capability of long short term memory (LSTM) prediction model. In the part of "on-line matching and assessment", the actual value time series (on-line data) of multistate parameters are decomposed and reconstructed by WPT. Each normalized sample is matched to the closest cluster centroid, which is generated in the part of "off-line training". Then the corresponding LSTM model is conducted to predict based on the each sample. The final prediction value is sum of prediction results of entire samples in clusters. Finally, the reliability of wind power converter is assessed by the proposed strategy. The effectiveness of proposed assessment strategy is verified by the experimental results on a PMSG wind power converter.

关键词： Reliability assessment wind power converter association rules algorithm deep learning recurrent neural network

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Discovering association rules Using R. A Case Study on Retail's Database 24th

Discovering Association Rules Using R. A Case Study on Retai...

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24th Argentina Congress of Computer Science (CACIC)

作者： Baez Acuna, Juan Manuel Paredes Cabanas, Clara Anuncia Sosa-Cabrera, Gustavo Garcia-Diaz, Maria E. Univ Nacl Asuncion Asuncion Paraguay

ISBN: (纸本)9783030207878;9783030207861

Today, the high competitiveness in retail businesses requires them to seek new strategies to ensure their survival. To this end, organizations have understood that the data located in their transactional databases can be used as raw material to boost business growth, if they can be exploited properly. The research's main objective is to apply Data Mining techniques for the discovery of association rules from purely commercial transactional data, taking as a study period 10-year in a household appliances and furniture retail entity. The selection's phase and preparation data are described as well as its cost in man/hours. In the modeling phase, the Apriori and Eclat algorithms implemented in the arules package of the R tool were executed, where both the resulting associations and execution time were compared. The results show relevant patterns in the buying behavior of customers such as those that relate items and accessories' prices.

关键词： Data mining association rules algorithm Retail Transactional databases R tool

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Research on the competitiveness of China's leisure sports industry based on statistical method

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2855-2860页

作者： Zheng, Yong China West Normal Univ Sch Phys Educ Inst Nanchong 637009 Peoples R China

Leisure sport is a rising industry in recent decades. Its development speed is far more than that of other industries. Despite the rapid development of sports industry, the number of leisure sports enterprises in China is still scarce. Therefore, this paper put forward the research on the competitiveness of leisure sports, combined the data of leisure sports industry with computer technology, and solved the problem of lagging development of sports in China through statistical operation. In order to optimize the model, we added the association rule algorithm to further improve the accuracy of data, so as to further expand the leisure sports market. Through a comprehensive test of the function of the algorithm, the results show that the system is feasible in the use of the system.

关键词： Statistics leisure sports association rules algorithm

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Research on Teaching Methods of Teachers' Ideological Education Based on Large Data

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ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS 2021年第3N04期13卷

作者： Chen, Jin Xi An Jiao Tong Univ City Coll Xian 710018 Shaanxi Peoples R China

The ideal form of modern education is one that is personalized toward the receiving student's aptitude, as derived from a large body of data. The most typical feature of large data is the complexity of data and the huge amount of data space. This paper analyses the data mining technology used to help optimize and execute the use of personalized education online to obtain useful information as well as gather data from real life situations. Based on the research of data set segmentation, the optimization of the key value for simplification is used to improve the efficiency of parallel computing. This paper analyses the characteristics of large data sets derived from digital personalized education, and data set processing for information obtained from user course frequency data, to improve the accuracy of mining association rules.

关键词： Large data association rules algorithm ideological education

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A data mining model to identify inefficient maintenance activities

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2013年第2期4卷 182-192页

作者： Mosaddar, Davood Shojaie, Amir Abbas Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran

Nowadays the data collected in the process in maintenance systems comprise a big portion of the related databases. Analyzing these maintenance data provides the firms, enterprises and organizations with a tremendous competitive edge both in manufacturing and service sectors. As maintenance management is a costly and inevitable part of the organization, ensuring that the maintenance activities are performed in an effective manner, is of outmost importance. In other words, organizations can precede with the cost reduction operations, for instance, if and only if the unproductive maintenance activities and processes can be identified. Subsequently, rectifying or removing these kinds of activities or taking other means of modification can help enterprises and organizations to reduce their costs. Data mining is known to be an excellent tool which helps the decision makers to discover the hidden knowledge and patterns when dealing with a large amount of data. Seeing a gap in the related literature reviewed and in order to fill it, this study proposes a data mining based model to identify the unproductive maintenance activities in a maintenance system. By identifying specific inefficient maintenance activities, this model supports the maintenance decision makers to set goals to make amendments in the maintenance systems under their supervisions. Consequently, the organizations can focus on rectifying these fruitless activities and therefore reducing the costs associated with performing them. Finally, the model was used to identify the unproductive activities in a maintenance system comprising of independent components (an urban bus network).

关键词： Maintenance systems Inefficient maintenance activities Data mining association rules Clustering algorithm association rules algorithm CRISP-DM

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