A fractional-order CNN hyperchaotic system for image encryption algorithm

Wang, Yanzhang; Yang, Feifei

doi:10.1088/1402-4896/abd50f

Cited by 47 publications

(32 citation statements)

References 32 publications

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“…First of all, this research needs to collect the action features, image features, and language features of Chinese martial arts culture, which is to prepare for the deep neural network technology. These data need to go through the data processing stage, and then the processed Chinese martial arts cultural characteristic data is input into the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) [ 30 , 31 ]. After the relevant features of Chinese martial arts culture are extracted by CNN and LSTM, the data is output after passing through the output layer.…”

Section: An Application Of Deep Neural Network In Chinese Martial Artsmentioning

confidence: 99%

The Research of Chinese Martial Arts Cross-Media Communication System Based on Deep Neural Network

Tian

Zan

2022

Computational Intelligence and Neuroscience

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The spread of Chinese martial arts is crucial for the world to understand Chinese culture. If only relying on one transmission method, it will lead to the difference of transmission and its lack of certain real time. This will lead to differences in the understanding of Chinese martial arts, which is also not conducive to the spread of Chinese glorious culture. Cross-media communication technology can solve this communication difference problem very well. The deep neural network method was used to fuse relevant features of Chinese martial arts, and it also analyzes the feasibility of neural network technology in cross-media communication. At the same time, this study uses deep neural network to study the timeliness of Chinese martial arts in the process of cross-media communication. The research results show that the convolutional neural network can effectively extract the characteristics of Chinese martial arts and carry out effective dissemination. However, the hybrid convolutional neural network with temporal features has higher accuracy in extracting Chinese martial arts features. This hybrid convolutional neural network is more conducive to the dissemination of Chinese martial arts through cross-media technology, which can ensure its timeliness. The maximum error of deep neural network technology in predicting Chinese martial arts culture is only 2.67%. This part of the error comes from the action characteristics of Chinese martial arts culture, which shows that neural network technology has good feasibility.

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Section: An Application Of Deep Neural Network In Chinese Martial Artsmentioning

confidence: 99%

The Research of Chinese Martial Arts Cross-Media Communication System Based on Deep Neural Network

Tian

Zan

2022

Computational Intelligence and Neuroscience

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“…CNN can have higher efficiency and computing power than manual methods, so it can handle the relationship between large amounts of data very well. Video methods will contain many image features, and CNN can have better feature extraction capabilities in both videos and images [9,10]. is research integrates videos and images to design a piano teaching mode, which mainly designs feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…It measures the difference in distance between the coordinate values of points in space. Equation (9) shows the Minkowski's distance measurement method. is is also a variant of the Euclidean and Chebyshev distances.…”

Section: Introduction To Piano Feature Data Classificationmentioning

confidence: 99%

Design and Application of the Piano Teaching System Integrating Videos and Images

Li¹

2022

Scientific Programming

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The art of piano playing has been continuously entering into people’s life. However, with the continuous improvement of science and technology and living standards, the traditional teaching mode can no longer meet the piano teaching mode. The teaching of piano is different from traditional subjects, such as Chinese and mathematics. It requires students to experience the artistic characteristics and the live atmosphere of the players brought by the piano. This study integrates video and image teaching methods with piano teaching. Videos and images can more intuitively show the live atmosphere brought by piano players and musical artistic features brought by the piano. At the same time, this study uses the convolutional neural network (CNN) method to study the relevant features of videos and images of piano teaching. These features are mainly the characteristics of piano music, the behavior of players, and the basic knowledge of a piano. The research results show that the clustering method can effectively classify the features of videos and images in piano teaching, and the maximum classification error is only 1.89%. The CNN method also has high performance in predicting the relevant features of piano teaching videos and images. Accuracy. The largest prediction error is only 2.23%, and the linear correlation coefficient also exceeds 0.95. This set of the piano teaching mode that combines videos and images is beneficial to both teachers and students.

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“…Big data technology has provided algorithms that can deal with spatial, temporal, and environmental correlations. In the quantitative analysis of the market development scale and the form of music innovation, it is mainly related to time and space, and the algorithms mainly include CNN and LSTM [12,13]. e development of the market is closely related to time data, and there is a large spatial correlation between the elements of market development scale and the mapping of elements of musical innovation forms.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

Zhang

Gong

2022

Discrete Dynamics in Nature and Society

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Music art is a form of conveying cultural information and humanistic emotions. From ancient times to the present, the form of music has undergone great changes. The change of music form is closely related to the change in dynasties, the development of the market economy, and the change in humanistic spirit. Today, the development of music has reached a relatively prosperous stage, which is closely related to the rapid development of the market economy. At the heart of the quantitative analysis is the study of associations between data. However, it will be a difficult task to analyze the relationship between the development scale of the market economy and the form of music innovation only by artificial means. This research mainly uses a convolutional neural network and long and short-term memory neural network technology to quantitatively analyze the relationship between the development scale of the market economy and the form of music innovation, which is mainly a quantitative analysis of classical music, pop music, and rap music. The research results show that convolutional neural networks and long short-term memory neural networks have sufficient capabilities to quantitatively analyze the relationship between the market development scale and the form of music innovation. Neural networks with LSTM layers have lower errors in predicting market economic correlates than neural network methods without LSTM layers. The error is reduced by 0.27%. For the prediction of innovative forms of music, the largest prediction error is only 2.93%, which is closely related to the variability of popular music. The linear correlation indices for the predictions of the three forms of musical innovation also all exceed 0.955.

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A fractional-order CNN hyperchaotic system for image encryption algorithm

Cited by 47 publications

References 32 publications

The Research of Chinese Martial Arts Cross-Media Communication System Based on Deep Neural Network

The Research of Chinese Martial Arts Cross-Media Communication System Based on Deep Neural Network

Design and Application of the Piano Teaching System Integrating Videos and Images

Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation

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