Analyzing body changes of high-level dance movements through biological image visualization technology by convolutional neural network

Zhang, Ruizhi

doi:10.1007/s11227-021-04298-y

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…After processing and sorting, it can form relatively standardized teaching materials for distribution and sale. erefore, video recording technology is widely used in Dance Teaching [15]. Lay explained the audio production and processing software, video processing software, image processing software, twodimensional and three-dimensional animation software, web page integration software, etc.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

Liu

2022

Journal of Environmental and Public Health

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As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art.

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Section: Related Workmentioning

confidence: 99%

[Retracted] An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

Liu

2022

Journal of Environmental and Public Health

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“…In formula (10), a and b represent coefficient terms and constant terms, respectively. In addition, it is given that z 1 k � 1/2(S 1 ik + S 1 i,k−1 ) k � 1, 2, 3, .…”

Section: Establishment Of Dance Talent Training Modelmentioning

confidence: 99%

“…e extrapolation of many benchmark chaotic structures and the time series is evaluated using a variety of neural and neuro-fuzzy models with various learning techniques in [1]. Lin et al [8][9][10] proposed the SANFN-GSE technique, which stands for the self-adaptive neural fuzzy network with group-based symbiotic evolution. Moreover, Barbounis et al[27] projected the LF-DFNN model (termed as locally feedback dynamic fuzzy neural network) for simulating the temporal processes.…”

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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“…In recent years, with the rapid development of neuroscience, artificial intelligence, and other technologies, the great potential of brain science has been highly valued, and the China Brain Project, which has been in the making for many years, has been launched. The development of brain science not only contributes to the prevention and treatment of brain diseases but also promotes the development of brain-like intelligence [ 3 ]. In the field of brain science, the simulation modeling of biological neurons and their networks occupies an important position and is a popular research direction in the field of brain science.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

Wang

Gao

Li³

2022

Computational Intelligence and Neuroscience

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In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficiency of knowledge graph-based recommendation methods in the face of relational sparse graphs, a pretraining-based implicit characterization algorithm strategy is proposed for the recall stage, which can solve the problems of difficulty in going online and high delay in the recall stage of the recommendation system while improving the accuracy, and not only this can be applied in the recall stage, but also the sorting and postsorting modules can be used as features. To study the relationship between signaling activity and energy metabolism of pyramidal neurons, an empirical model of the synaptic vesicle cycle is proposed to simulate the synaptic transmission process, the role played by energy metabolism in synaptic transmission is studied from the perspective of feedback control, and the quantitative relationship between neuronal pulse discharge frequency, energy consumption, and information quantity in dendritic integration is analyzed using the cable theory and atrial chamber model. It was found that, when 0 ≤ ε ≤ 0.6 , the chaotic region shrinks and eventually disappears with the increase of the memory factor ε ; however, when 0.6 ≤ ε ≤ 1 is used, chaos is recreated and the chaotic area gradually increases with the increase of the memory factor ε . This paper conducts comparative experiments on data sets in the recommendation domain and verifies that the proposed model and the feature intersection module can effectively perform feature interaction between items and entities, thus enhancing the recommendation effect.

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Analyzing body changes of high-level dance movements through biological image visualization technology by convolutional neural network

Cited by 12 publications

References 30 publications

[Retracted] An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

[Retracted] An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

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

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

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