Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom

Lu, Heping

doi:10.3233/jifs-189213

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…The theoretical research contribution lies in analyzing the influence of entrepreneurial network teaching on students’ learning psychology; the practical enlightenment is to design a college entrepreneurship teaching system based on HCI and apply it to college entrepreneurship teaching classrooms. Compared with the research results of Lu (2021) , the network entrepreneurship teaching and management system for young entrepreneurs based on HCI can have a positive impact on students’ entrepreneurial ability and entrepreneurial psychology, and help them acquire entrepreneurial knowledge and skills. This research provides a reference for exploring entrepreneurship education in colleges and universities.…”

Section: Discussionmentioning

confidence: 84%

“…Compared with the research results of Lu (2021), the network entrepreneurship teaching and management system for young entrepreneurs based on HCI can have a positive impact on students' entrepreneurial ability and entrepreneurial psychology, and help them acquire entrepreneurial knowledge and skills. This research provides a reference for exploring entrepreneurship education in colleges and universities.…”

Section: Discussionmentioning

confidence: 84%

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The Impact of Young Entrepreneurs’ Network Entrepreneurship Education and Management System Innovation on Students’ Entrepreneurial Psychology

2021

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The purpose of the study is to solve the problems existing in entrepreneurship education and management under computer technology. The teaching content of entrepreneurship education in colleges and universities is proposed. Since entrepreneurship education is practical, the auxiliary mechanism of entrepreneurship education also needs to be highly integrated with entrepreneurship practice. First, the network entrepreneurship teaching and management system is constructed, and students’ entrepreneurial creativity, communication ability, leadership ability, and qualities are taken as the research object. Second, the traditional teaching method, case study method, and scene simulation method are used to analyze and discuss the influence of the entrepreneurial teaching mode, entrepreneurial experience, and entrepreneurial ability on students’ entrepreneurial psychology. Finally, the questionnaire survey is used to conduct the relative sample t-test (Student’s t-test), and the influence of three teaching methods on students’ learning effects is analyzed. The influence of the three teaching methods on students’ entrepreneurial psychological states is further analyzed by the statistical method. The experimental results show that the test result of the scene simulation method and the traditional teaching method is 0.584, the test result of the scene simulation method and the case study method is 0.842, and the test result of the case study method and the traditional teaching method is 0.595. This shows that the scene simulation method has a significant impact on students’ entrepreneurial psychology and their entrepreneurial ability. In addition, students’ cognition of professional status significantly affects their entrepreneurial psychology and attitudes, and the correlation coefficient is 0.576. Therefore, it is suggested that colleges and universities should adopt the scene simulation method to improve the teaching quality of entrepreneurship education and strengthen students’ cognition of professional status and their entrepreneurial practice.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 84%

The Impact of Young Entrepreneurs’ Network Entrepreneurship Education and Management System Innovation on Students’ Entrepreneurial Psychology

2021

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“…Among them, C > 0 is a compromise parameter, indicating the degree of error separation that can be tolerated within the maximum interval [19,20].…”

Section: Bp Neural Network and Its Algorithmmentioning

confidence: 99%

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Zhao

Wang

2022

Mobile Information Systems

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Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.

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“…From the simplest linear regression to the most popular artificial neural networks, they are "learning algorithms," because these algorithms are algorithms that obtain laws from data, and "deep learning algorithms" are a kind of learning algorithms, which can almost replace "artificial neural networks" in practical use [8]. Among them, "depth" is relative to the traditional two-or three-tier artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Web Page Advertising Design Style

Deng¹,

Yang

2022

Security and Communication Networks

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With the development of deep learning algorithm in Internet web pages and customers’ demand for web advertising design, different businesses begin to pay attention to web advertising design style. In order to predict and promote the development of web advertising design style, this paper studies the establishment process of web design model based on deep learning algorithm in detail. With the application of this model to the web advertising design of different businesses, this paper compares and analyzes the differences of web advertising design styles of different businesses and puts forward corresponding suggestions on web advertising design styles, which promotes the diversified development of web advertising design styles to a certain extent and has a good application effect.

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Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom

Cited by 19 publications

References 10 publications

The Impact of Young Entrepreneurs’ Network Entrepreneurship Education and Management System Innovation on Students’ Entrepreneurial Psychology

The Impact of Young Entrepreneurs’ Network Entrepreneurship Education and Management System Innovation on Students’ Entrepreneurial Psychology

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Application of Deep Learning Algorithm in Web Page Advertising Design Style

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