Analysis of multi-level capital market linkage driven by artificial intelligence and deep learning methods

Jing, Xinxin; Peng, Pai; Huang, Zhe

doi:10.1007/s00500-019-04095-z

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…Artificial intelligence technology has great advantages in processing tedious data, which has been proven in many fields [13,14]. e content and propagation path of selfmedia technology can be converted into the form of data for relevant prediction [15,16]. erefore, artificial intelligence methods can bring certain benefits to the prediction of the content and propagation paths of self-media videos.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Self-Media Film and Television Content Production and Dissemination Paths under the Background of Artificial Intelligence

Zhang

Meng

2022

International Journal of Antennas and Propagation

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The development of Internet technology and cloud computing has promoted the rapid development of self-media technology. The self-media technology has better people-friendliness compared to the traditional media communication mode, so it has gained more popularity compared to the traditional media mode. For professional self-media teams, the popularity and clicks of self-media content are very critical. It needs to make corresponding predictions and judgments according to the content and transmission path of the self-media. However, self-media is transmitted in the form of video, which involves a huge amount of data. This team of self-media staff is a more difficult and tedious task. This study uses the atrous convolution and long short-term memory neural network to predict the video content, sound features, and propagation path of self-media technology. Atrous convolution is more suitable for research objects with more data. The research results show that the atrous convolution and LSTM methods have better feasibility and credibility in predicting three special characteristics of self-media. Compared with a single atrous convolution, the feature prediction errors of the three kinds of self-media are smaller by using the atrous convolution and LSTM hybrid method. The largest prediction error is 2.39%, and this part of the error is mainly due to the sound characteristics of self-media technology.

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Section: Introductionmentioning

confidence: 99%

Optimization of Self-Media Film and Television Content Production and Dissemination Paths under the Background of Artificial Intelligence

Zhang

Meng

2022

International Journal of Antennas and Propagation

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“…With the continuous advancement of science and technology, there will be huge amounts of data in every field. e larger the magnitude of the data, which requires deeper CNN to complete the feature extraction task [13,14].…”

Section: Introductionmentioning

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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“…e development of big data technology also has an important relationship with the computing performance of computers. In recent years, the computing performance of computers and the development of hardware devices have also promoted the rapid development of big data technology [10,11]. Today's computer has enough performance to support the operation of data in most fields.…”

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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Analysis of multi-level capital market linkage driven by artificial intelligence and deep learning methods

Cited by 16 publications

References 15 publications

Optimization of Self-Media Film and Television Content Production and Dissemination Paths under the Background of Artificial Intelligence

Optimization of Self-Media Film and Television Content Production and Dissemination Paths under the Background of Artificial Intelligence

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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