scholarly journals Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoling Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Feature Recognition ◽  
Evaluation Method ◽  
Training Model ◽  
Recognition System ◽  
Big Data Analysis ◽  
Classification Algorithm ◽  
Deep Belief Network ◽  
Belief Network

Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

scholarly journals Application of Multiacoustic Data in Feature Extraction of Anemometer

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dawei Chen ◽  
Xu Guo
Keyword(s):  
Feature Extraction ◽  
Wind Power ◽  
Evaluation Method ◽  
Training Model ◽  
Classification Algorithm ◽  
Deep Belief Network ◽  
Evaluation Function ◽  
Trust Network ◽  
Belief Network ◽  
Acoustic Data

The acoustic characteristics of wind instruments are a major feature in the field of vocal music. This paper studies the application effect of wind power instrument feature extraction based on multiacoustic data. Combined with the acoustic data training model, the classification algorithm based on deep trust network is used to process multiple acoustic data. Using multiple acoustic data for feature extraction, the recognition and matching between multiple acoustic data and wind measuring instrument are realized. The experiment not only evaluates the error of the network classification algorithm but also describes the evaluation function of the deep belief network classification algorithm in the system. The traditional SNR evaluation method is used to improve the deficiency of evaluation function. Through the deep belief network classification algorithm for self-learning, the instrument recognition method with strong applicability is established. Finally, the effectiveness of multiacoustic data in wind power instrument feature extraction is verified.

scholarly journals Influencing Factors and Forecasting Statistics of Enterprise Market Sales Based on Big Data and Intelligent IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhen Guo ◽  
Tao Zou
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Classification Algorithm ◽  
Processing Method ◽  
Enterprise Management ◽  
Training Process ◽  
Data Processing Method ◽  
Knn Classification ◽  
Positive Effect

With the acceleration of economic development, enterprise management is facing more severe challenges. Big data analysis based on the intelligent Internet of Things (IoT) has a positive effect on the development of enterprise management and can make up for the shortcomings of enterprise management. In this paper, we develop a big data processing method based on intelligent IoT which can mine the factors that affect the company’s market sales from the collected data. Then, we propose a KNN classification algorithm based on overlapping k -means clustering. This algorithm adds a training process to the traditional KNN algorithm, which can accurately classify data and greatly improve the efficiency of the classification algorithm. Numerical analysis results prove the effectiveness of the proposed algorithm.

scholarly journals Copying and Recreation Methods of Painting Works Relying on Mobile Digital Multimedia Big Data Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Li Xue ◽  
Chuangjian Yang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Image Coding ◽  
Feature Recognition ◽  
Big Data Analysis ◽  
Simulation Methods ◽  
Multimedia Big Data ◽  
Digital Multimedia ◽  
Processing Module ◽  
Big Data Technology

In order to improve the effect of copying and recreation of painting works, this paper combines mobile digital multimedia big data technology to improve the image coding algorithm, identify the characteristics of existing works, apply the algorithm to the detailed analysis of painting works, and construct the main functional structure modules of the system. Moreover, this paper combines the existing hardware equipment to construct the painting works’ recreation system and obtains the image processing module. After the system is constructed, the effect of copying and recreating painting works is analyzed through the mobile digital multimedia big data analysis technology. Finally, this paper constructs the system of this paper through simulation methods and uses experiments to calculate the feature recognition effect and copy effect of the painting works of the system. Through experimental analysis, it can be known that the copying and recreation system of painting works based on mobile digital multimedia big data analysis proposed in this paper can help painters effectively improve the effect of recreation.

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