[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

School sports is an important part of sports. The participants are mainly school students. Students play an important role in the development of the country. The development of campus sports is particularly important for the future development of the country. With the continuous improvement of the level of sports technology, to achieve higher, faster, and stronger sports goals, the requirements for sports venues continue to increase. To study whether the environment of the school’s badminton court meets the requirements of the school’s teachers and students to exercise and whether it can achieve the purpose of improving their own sports skills, the above problems are studied. Through the analysis of the light environment in the school grounds, to judge some problems existing in the badminton field of the school, improvement measures are put forward through research, and feasibility analysis of the improvement measures is carried out.

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“…Light intensity has a great influence on the photosynthesis of organisms. It can be measured by a light meter [10][11][12][13][14][15]. (…”

Section: Results Analysismentioning

confidence: 99%

The Comfort Analysis Model Design of the Ground Light Environment of a Dome Reflective Badminton Court in a University

Ding

Wang

Wu³

2022

Scientific Programming

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“…Trade companies can transfer credit risks to credit risk institutions when foreign trades are conducted to ensure the normal operation of trade activities [17]. Domestic research on export credit risks starts late, the initial research on the trade credit risk shows that the contract risk is the most important export credit risk [18]. Some studies hold that the export credit risk comes from the unpaid bills of foreign importers due to their reasons [19].…”

Section: Export Credit Risk Assessmentmentioning

confidence: 99%

Application of Deep Belief Network for Sustainable Development via Deep Learning to Export Credit Risk Assessment Under the Belt and Road Strategy

Li,

Pu,

Song

et al. 2023

Advances in Transdisciplinary Engineering

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The purpose of the study is to reduce the export credit risk of enterprises, and the export credit risk assessment of enterprises under the belt and road strategy based on deep learning is discussed. First, the research background and deep belief networks are introduced. Second, the contrast divergence algorithm based on the deep belief model is improved on the restricted Boltzmann machine. Finally, the deep belief network of classification and partition is constructed and simulated. The results show that the test accuracy of the classification and partition of the restricted Boltzmann machine (CPRBM) constructed is higher than that of the restricted Boltzmann machine (RBM). When the accuracy of the algorithm is verified under the condition of unbalanced two classification samples in a relatively small amount of datasets, the accuracy of the CPRBM algorithm is 93.71%, and the accuracy of the RBM algorithm is 89.86%. In the optimization stage of the deep belief networks, the convergence rate of the CPRBM is slower than that of the RBM. Since the optimized system increases the penalty term in the first training stage of the deep belief networks, the penalty term is canceled in the second stage of optimization. At three time points, the algorithm accuracy of the CPRBM is higher than that of the RBM. The simulation results are consistent with the previous experimental results. Although the accuracy is not high at the third time point, the CPRBM algorithm still has some advantages. Compared with the accuracy of the support vector machine (SVM) and the deep extreme learning machine (DELM), the CPRBM algorithm based on deep belief networks has the highest accuracy at any time point. The CPRBM algorithm constructed has obvious advantages compared with common models, and the overall performance of the algorithm is better. The conclusions provide the support for the sustainable development of the economy under the Belt and Road strategy.

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“…Dang used the deep belief neural network algorithm to design and the in-depth analysis of the sport venue operation risk early warning model. Compared with spatiotemporal graph convolutional neural networks, deep belief neural networks use different feature information [9]. From the content of Ramadhani's research, it can be understood that the methods used in his research are descriptive and qualitative data collection methods [10].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Simulation and Comprehensive Quantitative Evaluation of Rural Tourism Core Competitiveness Based on Neural Network

Gan

2022

Advances in Multimedia

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Rural tourism is the purpose of vacation tourism, which is an important support for rural economic development. Neural network algorithms are the second way to simulate human thinking. The purpose of this paper is to obtain benefits and diversify risks through dynamic simulation analysis and comprehensive quantitative analysis based on the neural network algorithm, so as to improve the core competitiveness of rural tourism. This paper first designs the neural network algorithm model, then analyzes the dynamic simulation model and comprehensive quantitative analysis, and then uses the artificial neural network algorithm to analyze and predict the core competitiveness of rural tourism. The results confirm the effectiveness of the algorithm. Under the demand forecast of the core competitiveness of rural tourism, using the artificial neural network algorithm, taking city A as an example, the number of tourists in rural tourism in city A from 2017 to 2021 was analyzed. The year with the largest number of people is 2021, with 2,586,000 people, and the year with the largest growth rate is 2019, with 2,576,900 people, a growth rate of 24.16%. Comparing the experimental data of each group, the results show that the algorithm has high efficiency in solving problems and also confirms the scientific validity of the algorithm.

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[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

Cited by 7 publications

References 25 publications

The Comfort Analysis Model Design of the Ground Light Environment of a Dome Reflective Badminton Court in a University

The Comfort Analysis Model Design of the Ground Light Environment of a Dome Reflective Badminton Court in a University

Application of Deep Belief Network for Sustainable Development via Deep Learning to Export Credit Risk Assessment Under the Belt and Road Strategy

Dynamic Simulation and Comprehensive Quantitative Evaluation of Rural Tourism Core Competitiveness Based on Neural Network

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