[Retracted] Risk Prediction of Sports Events Based on Gray Neural Network Model

Wang, Zhihui

doi:10.1155/2021/6214036

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Generally speaking, the event detection needs to define an event pattern first and then match the content of the video with this event pattern. If it matches the pattern, it means that the event happened; otherwise, it means that the event did not happen [ 17 ]. If there are too many feature choices, on the one hand, it will affect the efficiency of motion mining; on the other hand, it may also have a negative impact, resulting in the mined knowledge deviating from the real situation.…”

Section: Methodsmentioning

confidence: 99%

Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

2022

Computational Intelligence and Neuroscience

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Mass sports has become a world trend, setting off a new health revolution in the world. Mass fitness programs not only enrich people's lives. It not only relieves the psychological pressure of modern people but also promotes people's health and improves people's quality of life. According to the time-consuming stability of neural network algorithm, this paper proposes a sports video recognition algorithm based on BP neural network. The static and dynamic features are classified by BP neural network, and the basic probability assignment is constructed according to the preliminary recognition results. At the same time, we use evidence theory to fuse the preliminary results and get the results of motion video recognition. It can be applied to the generation model of the feasible scheme of mass sports fitness. Relevant experiments show that the whole model that generates the feasible mass sports fitness scheme can accurately generate the sports fitness scheme of multiple patient users and ensure the rationality and safety of the sports fitness scheme.

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Section: Methodsmentioning

confidence: 99%

Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

2022

Computational Intelligence and Neuroscience

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“…Recently, time-series analysis [20] has been widely used in the fields of medicine [21,22], risk prediction [23,24], and agriculture [25,26], which is attributed to its ability to model and analyze historical information to predict upcoming tendencies The quality variation of the wheat storage process is closely related to the influencing factors with time-series characteristics, so there are more and more scholars of time-series analysis to predict and evaluate the quality of wheat storage and its related aspects. Jeong et al [27] employed the random forest (RF) algorithm, which integrated four climatic variables and seven additional biophysical variables, to accurately forecast wheat yields across various regions worldwide.…”

Section: Introductionmentioning

confidence: 99%

Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage

Liu

Zhang

Dong

et al. 2023

Foods

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Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index Q was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat.

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“…Time series data refers to the sequence formed by the values of different indicators of an object or phenomenon at different times arranged in chronological order. This way of recording data is the most commonly used in actual production and life [ 4 ]. The analysis methods of time series mainly originated from the stochastic process theory and statistics category.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

Gui-cai

2022

Computational Intelligence and Neuroscience

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With the extreme deterioration of domestic and foreign trade environment, international competition is becoming increasingly fierce. At the same time, many enterprises have loopholes in industrial structure and finance, resulting in many risks in their international trade. Therefore, we must take corresponding measures to effectively manage and avoid risks and realize the healthy and sustainable development of foreign trade enterprises and enterprise economy. This paper designs and proposes a risk prediction model combining ARIMA and BP neural network. The model can get good prediction in different time series and effectively avoid the risk. The model proposed in this paper optimizes the structure of the design model with the support of ARIMA algorithm and BP neural network algorithm and has good accuracy and error control for different time series. The purpose of establishing time series prediction model is to improve the prediction accuracy of the model, and it is also an effective way to enhance the practicability of the prediction model. Applying intersequence analysis method to financial risk prediction can greatly improve efficiency and save cost and has broad application prospects.

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[Retracted] Risk Prediction of Sports Events Based on Gray Neural Network Model

Cited by 6 publications

References 25 publications

Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

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