Multi-Index Classification Model for Loess
Deposits Based on Rough Set and BP
Neural Network

Zhang, Xueliang; Xue, Yiguo; Qiu, Daohong; Yang, Weimin; Mao-xin, SU; Li, Zhiqiang; Zhou, Binghua

doi:10.15244/pjoes/85303

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…Zhang et al thought that the responsibility of university in the new era is to enhance students' entrepreneurial awareness and EA and guide students to learn the skills of market operation in a reasonable curriculum system under the guidance of correct objectives, so as to enhance their ability to start their own businesses [17]. He et al believed that the emergence of innovative education is an educational idea accompanied by the rise of knowledge economy.…”

Section: Research Status Of College Students' Ea Evaluationmentioning

confidence: 99%

[Retracted] Model Construction of College Students’ Entrepreneurial Ability Cultivation in Mental Health Education Environment

Huang

2022

Journal of Environmental and Public Health

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National policies have been very supportive of college students as a new force in IEE (innovation and entrepreneurship education). Universities across the nation have increased their emphasis on entrepreneurship education and put a lot of effort into developing and enhancing college students’ EA (entrepreneurship ability). An important challenge facing universities is how to develop the psychological quality of IEE in college students and create a long-term training system. The author develops the EA evaluation model for college students based on practical experience with mental health education and applies BPNN (BP neural network) theory to the assessment of IEE in higher education. The scores corresponding to the students’ evaluation indices are used as input vectors, and the innovative EA of the students is evaluated. According to the simulation’s outcomes, the number of hidden neurons is calculated, and the outcomes are assessed through experiments. The outcomes demonstrate that, following the completion of the enhanced BPNN training, the actual value is valid, the expected value is reasonable, and the maximum relative error between them is 1.6 percent, which is essentially the ideal situation. It has been established that the IEE’s established evaluation index system is fair and can accurately capture students’ IEE spirit.

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Section: Research Status Of College Students' Ea Evaluationmentioning

confidence: 99%

[Retracted] Model Construction of College Students’ Entrepreneurial Ability Cultivation in Mental Health Education Environment

Huang

2022

Journal of Environmental and Public Health

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“…Computational and Mathematical Methods in Medicine intelligent classification of loess deposits in tunnel engineering, Zhang and others proposed an evaluation system based on BP neural network, which was applied to practical analysis and showed that the model output results were consistent with the actual results [29].…”

Section: Bp Neural Networkmentioning

confidence: 82%

“…The results show that the overall accuracy of the fault diagnosis model based on the improved BP neural network is as high as 98.3% [ 28 ]. In order to realize the intelligent classification of loess deposits in tunnel engineering, Zhang and others proposed an evaluation system based on BP neural network, which was applied to practical analysis and showed that the model output results were consistent with the actual results [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Monitoring of Serum Protein in Acute Respiratory Distress Syndrome Based on Artificial Neural Network

Zhou

Long

2022

Computational and Mathematical Methods in Medicine

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Acute respiratory distress syndrome (ARDS) is one of the more serious diseases in human lung disease. Reducing its incidence rate is an important task in current clinical research. Dynamic monitoring of serum protein in patients will help to achieve the early diagnosis and treatment of ARDS. In this study, a protein monitoring model based on artificial neural network is proposed. First, surface enhanced laser desorption ionization time-of-flight mass spectrometry is used for protein detection, and then BP neural network is used for protein classification and content analysis. In the experimental analysis, serum samples from patients with acute respiratory distress syndrome in our hospital from November 2020 to August 2021 were selected for experimental testing. The experimental results show that the serum protein monitoring model based on BP neural network has low error and high convergence ability and can monitor individual protein in protein monitoring, and the area under the ROC curve in diagnostic performance reaches 0.854. The above results show that the artificial neural network has a good effect on the dynamic monitoring of serum protein in acute respiratory distress syndrome, and the diagnostic performance evaluation can reach 0.854, which has the ability to significantly improve the clinical diagnosis and treatment of acute respiratory distress syndrome.

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“…e trained BPNN model is valid if the error is within this range, ending the training process. If the error is not within the set accuracy, then the inner coefficients in the hidden layers need to be modified again [27,28]. First, the input increment in the hidden layer is set by equations ( 17) and (18).…”

Section: Algorithm Process Of the Bpnnmentioning

confidence: 99%

Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency Evaluation

Yang

Mei

et al. 2022

Computational Intelligence and Neuroscience

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The research performed here intends to explore the future development model of new rural financial institutions and determine the financial efficiency goals, thereby providing a huge stage for the development of new rural financial institutions. It applies data envelopment analysis (DEA) to assess financial efficiency to make up for the research gap. First, the relevant theories of rural finance are discussed. Then, some indicators are selected to build an evaluation system. In addition, the DEA method is used to evaluate the rural financial efficiency in Hebei Province by listing the input indexes and output indexes. After training, the backpropagation neural network (BPNN) model is designed and simulated to obtain the evaluation results. The research results show that before 2015, the comprehensive efficiency of Xingtai, Hengshui, Shijiazhuang, and Langfang showed a downward trend. After 2015, the comprehensive efficiency of all cities in Hebei Province tended to be stable, generally stable at about 0.95. It suggests that rural finance in Hebei Province has developed stably in recent years, and the overall efficiency of rural finance has been improved to a certain extent. The simulation results of BPNN demonstrate that the operation efficiency evaluation result of the poverty alleviation development model in the financial support industry is 0.6995, in the interval (0.6, 0.8). In addition, the model operation efficiency is better, indicating that the poverty alleviation development model of the financial support industry has achieved good results and promoted the development of poor rural areas. The research content can provide reference and inspiration for the continuous promotion of rural finance and the formulation and implementation of financial institution reform policies.

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Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

Cited by 13 publications

References 39 publications

[Retracted] Model Construction of College Students’ Entrepreneurial Ability Cultivation in Mental Health Education Environment

[Retracted] Model Construction of College Students’ Entrepreneurial Ability Cultivation in Mental Health Education Environment

Dynamic Monitoring of Serum Protein in Acute Respiratory Distress Syndrome Based on Artificial Neural Network

Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency Evaluation

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