Cheerleading athlete's action safety in sports competition based on Kohonen neural network

Chen, Bingxin; Kuang, Lifei; He, Wei

doi:10.1007/s00521-022-07133-4

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…In order to solve this problem, the unsupervised learning method has become an attractive choice. Among them, the Kohonen network model, as a classic self-organizing map algorithm, can build a cluster structure according to the characteristics of equipment and consider the difference of surplus value [13]. This paper aimed to explore the construction of overdue medical device model based on Kohonen network algorithm.…”

Section: Kohonen Network Model Constructionmentioning

confidence: 99%

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Tan,

Mao,

Zhang

et al. 2023

Preprint

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With the continuous updating and progress of medical equipment, overdue medical device has problems such as management difficulties, resource waste and potential security risks. Therefore, this paper used Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, Kohonen network algorithm was used to build a quantitative model of the surplus value of overdue medical device, and the self-organization characteristics and data-driven learning ability of Kohonen network were used to more accurately predict the surplus value of equipment. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical device, and further optimize the model performance, so as to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper well evaluated the correlation between the service life and maintenance cost of eight types of overdue medical device, and quantitatively predicted the surplus value of overdue medical device with the random forest algorithm. According to the comparison of prediction bias, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing decision tree algorithm, the average error rate of Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical device, thus improving the efficiency of medical equipment management, reducing costs and ensuring patient safety.

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Section: Kohonen Network Model Constructionmentioning

confidence: 99%

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Tan,

Mao,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Lin and Lee [2] suggested an RNN-FLCS, or reinforcement neural-network-based fuzzy logic control system, to address a variety of reinforcement learning issues. For the implementation of a fuzzy logic control system, a general connectionist model known as the neural fuzzy control network (NFCN) is suggested [3][4][5][6]. Similarly, a developing fuzzy neural network (termed as EFuNN) along with an arti cial neural network (ANN) model which is trained by means of the scaled conjugate gradient procedure (CGA) and the well-known backpropagation (BP) procedure is presented, and these are the widespread soft computing techniques taken into consideration in the research conducted in [7].…”

Section: Introductionmentioning

confidence: 99%

The Prediction Model of Dance Talent Training in Fuzzy Neural Network Algorithm

Zhong

2022

Mobile Information Systems

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In view of the existing problems in the training of dance talents, particularly in China, the focal and key goal of this investigation is to examine and train a predictive model using the fuzzy neural network algorithm. In order to enhance the training, improve the quality of dance talents, and further promote the development of dance professional education, this paper implements the fuzzy neural network procedure to train prediction models for dance talents. Moreover, we carry out research and, then, establish a prediction model, through the fuzzy neural network algorithm, to predict the quality and effect of dance talent training. The model can be, then, used to deliver a fundamental and a key reference for the training of dance talents in the social sectors across the world. The experimental setup and obtained outcomes show that the suggested algorithm has good usability for the training and prediction of dance talents in terms of accuracy. We observed that the fuzzy-based technique is approximately 17.6% more precise than the classical scheme. Moreover, the prediction correctness was observed more than 98.5%.

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Surplus value quantification of overdue medical devices based on Kohonen network algorithm

Tan,

Mao,

Zhang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

With the continuous updating and progress of medical equipment, the overdue medical device has problems such as management difficulties, resource waste, and potential security risks. Therefore, this paper used the Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, the Kohonen network algorithm was used to build a quantitative model of the surplus value of the overdue medical device, and the self-organization characteristics and data-driven learning ability of the Kohonen network were used to predict the surplus value of the equipment more accurately. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical devices, and further optimize the model performance, to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper evaluated the correlation between the service life and maintenance cost of eight types of overdue medical devices and quantitatively predicted the surplus value of overdue medical devices with the random forest algorithm. According to the comparison of prediction bias, the maximum deviation between the expected surplus value and the actual surplus value is only 1, and the deviation value by the random forest algorithm is as low as 6, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing the decision tree algorithm, the average error rate of the Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of the random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical devices, thus improving the efficiency of medical equipment management, reducing costs, and ensuring patient safety.

show abstract

Cheerleading athlete's action safety in sports competition based on Kohonen neural network

Cited by 5 publications

References 22 publications

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

The Prediction Model of Dance Talent Training in Fuzzy Neural Network Algorithm

Surplus value quantification of overdue medical devices based on Kohonen network algorithm

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