[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu, Xiaoling

doi:10.1155/2021/5609885

Cited by 7 publications

(6 citation statements)

References 25 publications

(27 reference statements)

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“…This three-dimensional planning method, combined with mathematical methods and information methods, was early applied to solve the problems of three-dimensional restoration of ancient buildings and planning and management of ancient buildings in small towns [ 24 ]. Therefore, the planning and management of small town ecological landscape through HQR three-dimensional planning model is to determine the mathematical relationship between many factors of grey system and realize the reconstruction of landscape facilities from 2D to 3D [ 25 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Research on Town Ecological Landscape Planning and Governance Based on Fuzzy Optimization Method of Internet of Things Technology

Ying

Jiang

Ren

2022

Computational Intelligence and Neuroscience

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The current town ecological landscape planning and governance methods are mainly based on the high quality, high energy-saving, and environmental protection effect of the town ecological landscape. How to innovate the town ecological landscape planning and governance process with the help of Internet of things technology and fuzzy optimization method is the current development trend. Based on this, this paper studies the application of Internet of things technology in town ecological landscape planning and management. Firstly, a small town ecological landscape evaluation model based on fuzzy optimization algorithm is proposed. Combined with multivariate matrix transformation function, the authenticity data of ecological landscape are simulated. The original analysis of different types of small town ecological landscape is realized by selecting the multivariate extremum of autocorrelation function curve in the process of planning and governance. Secondly, in the simulation evaluation link, the fuzzy evaluation method is adopted and improved. At the same time, the improved three-dimensional original planning governance model is used to comprehensively analyze the simulation results of three-dimensional landscape planning governance. Finally, by designing fuzzy simulation experiments, the application effects of different Internet of things technologies in town ecological landscape planning and governance are analyzed. The experimental results show that the correlation data indicators of fuzzy optimization methods corresponding to different Internet of things technologies are very different. The application effect of different types of Internet of things technology in ecological landscape planning and governance of small towns is targeted and shows strong regularity.

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Section: Methodsmentioning

confidence: 99%

Research on Town Ecological Landscape Planning and Governance Based on Fuzzy Optimization Method of Internet of Things Technology

Ying

Jiang

Ren

2022

Computational Intelligence and Neuroscience

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“…In recent years, with the deepening of the research on intelligent classification algorithm and big data analysis of the Internet of things, its application in various fields is also gradually increasing, so speech recognition is starting to combine with more algorithms [ 21 ]. For example, in the fields of medicine, science and technology, transportation, agriculture, economy, and so on, it can be combined with the hardware sensors in the Internet of things to complete the collaborative processing and quantitative analysis of data in different dimensions [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

Xue

2022

Computational Intelligence and Neuroscience

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With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.

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“…When each element in Y is given an optimized weight [12], we can not only retain the number of features we need, but also highlight the role of useful elements in the features. Therefore, the particle dimension is equal to the feature dimension.…”

Section: Basic Principle Of Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Feature recognition of English clauses based on particle swarm optimization algorithm

2023

jeis

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Feature recognition of English clauses is a basic problem of syntactic analysis. It is the basis of English-Chinese machine translation. A feature recognition method of English clauses based on particle swarm optimization algorithm is proposed. This paper analyzes the characteristics of English clauses, delimits the boundary of clauses, and follows the current optimal particle in the solution space to search the best position through the cooperation and information sharing between particle swarm individuals. The feature set is selected, the crossover and mutation idea of genetic algorithm is introduced, and the crossover operation is carried out to complete the feature recognition of English clauses. The experimental results show that when the threshold P is 50, the recognition accuracy of this algorithm is consistent with that when p is 100, and the recognition accuracy is 93.45%. The accuracy of particle swarm optimization algorithm for English clause feature recognition is high, which remains at about 90%. Compared with the two literature methods, the convergence performance of particle swarm optimization algorithm is better.

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[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Cited by 7 publications

References 25 publications

Research on Town Ecological Landscape Planning and Governance Based on Fuzzy Optimization Method of Internet of Things Technology

Research on Town Ecological Landscape Planning and Governance Based on Fuzzy Optimization Method of Internet of Things Technology

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

Feature recognition of English clauses based on particle swarm optimization algorithm

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