Research on human performance evaluation model based on neural network and data mining algorithm
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 dat...
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| Veröffentlicht in: | EURASIP journal on wireless communications and networking Jg. 2020; H. 1; S. 1 - 14 |
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| Sprache: | Englisch |
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Cham
Springer International Publishing
10.09.2020
Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-1499, 1687-1472, 1687-1499 |
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| 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 the 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. 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. A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes, and transmits the information of the perceived objects in the geographical area covered by the network and finally puts these The information is sent to the owner of the network. This algorithm improves the accuracy of personnel performance evaluation, 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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| AbstractList | 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 the 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. 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. A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes, and transmits the information of the perceived objects in the geographical area covered by the network and finally puts these The information is sent to the owner of the network. This algorithm improves the accuracy of personnel performance evaluation, 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. 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 the 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. 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. A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes, and transmits the information of the perceived objects in the geographical area covered by the network and finally puts these The information is sent to the owner of the network. This algorithm improves the accuracy of personnel performance evaluation, 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. |
| ArticleNumber | 174 |
| Author | Li, Tingyi Liang, Wei |
| Author_xml | – sequence: 1 givenname: Wei surname: Liang fullname: Liang, Wei organization: Shandong Agriculture and Engineering University, Division of Business Administration, Wonkwang University – sequence: 2 givenname: Tingyi surname: Li fullname: Li, Tingyi email: cuojiepangen1998@163.com organization: Division of Business Administration, Wonkwang University |
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| Copyright | The Author(s) 2020 The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | Wireless sensor networks BP neural network Government administrators Regression analysis Analytic hierarchy process |
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| Snippet | In order to effectively evaluate personnel performance, a distributed data mining algorithm for spatial networks based on BP neural wireless network is... Abstract In order to effectively evaluate personnel performance, a distributed data mining algorithm for spatial networks based on BP neural wireless network... |
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| SubjectTerms | Accuracy Adaptive filters Algorithms Algorithms and Architectures for Industrial Wireless Sensor Networks Analytic hierarchy process BP neural network Cloud computing Communications Engineering Data analysis Data compression Data mining Data processing Data structures Empirical analysis Engineering Excavators Feature extraction Government administrators Human performance Information Systems Applications (incl.Internet) Networks Neural networks Object recognition Optimization Performance evaluation Personnel Regression analysis Signal,Image and Speech Processing Spatial data Statistical analysis Wireless networks Wireless sensor networks |
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| Title | Research on human performance evaluation model based on neural network and data mining algorithm |
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| Volume | 2020 |
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