Likability of human voices: A feature analysis and a neural network regression approach to automatic likability estimation

As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural network algorithms. The results show that the multilayer self-organizing neural network can make the accuracy, recall, and F1 score of feature recognition reach 68.69%, 80.21%, and 70.19%, respectively, when there are many samples. Under the influence of strong noise, it keeps high efficiency of feature analysis. This shows that the multilayer self-organizing neural network has superior performance and can provide strong support for Chinese language feature analysis.

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Slow Feature Analysis Based on Convolutional Neural Network for SAR Image Change Detection

10.1109/igarss47720.2021.9553912 ◽

2021 ◽

Author(s):

Ling Wanab ◽

Lei Maab ◽

Jialong Guoad ◽

Mingliang Liuac ◽

Dongpan Yao Ab

Keyword(s):

Neural Network ◽

Change Detection ◽

Convolutional Neural Network ◽

Feature Analysis ◽

Sar Image ◽

Slow Feature Analysis ◽

Image Change Detection

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Dynamic response prediction of water medium explosion container based on convolutional neural network

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-204589 ◽

2020 ◽

pp. 1-10

Author(s):

Linna Li ◽

Chenchen Fang ◽

Dongwang Zhong ◽

Li He ◽

Jianfeng Si

Keyword(s):

Neural Network ◽

Dynamic Response ◽

Convolutional Neural Network ◽

Bp Neural Network ◽

Response Prediction ◽

Feature Analysis ◽

Water Medium ◽

Strain Response ◽

Bp Neural Network Model ◽

Different Doses

The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel.

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