Integration of Museum User Behavior Information Based on Wireless Network

Liu, Shuangji; Yang, Yaling; Wang, Yiwei

doi:10.1155/2021/6847144

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…At the same time, the word vector method can overcome the problems of high vector dimension and data sparseness in grammar models. This is a big advantage that text representation does not have, so using word vector method to represent text can improve the quality and accuracy of distributed literature in digital libraries [18].…”

Section: Text Classification Based On Deepmentioning

confidence: 99%

Digital Library Information Integration System Based on Big Data and Deep Learning

2022

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In order to solve the defects of traditional text classification in digital library, the author proposes a method based on deep learning in the field of big data and artificial intelligence, which is applied to the digital library information integration system. On the basis of systematically sorting out the traditional text classification of digital library of this method, this paper proposes a digital library text classification model based on deep learning and uses the word vector method to represent text features, the convolutional neural network in the deep learning model is used to extract the essential features of text information, and experimental verification is carried out. Experimental results show that deep learning-based text classification model can effectively improve the accuracy (average 94.8%) and recall (average 94.5%) of text classification in digital libraries; compared with the traditional text classification method, the text classification method based on deep learning improves the average F1 value by about 11.6%. Conclusion. This method can not only improve the intelligence of the internal business of the digital library, but also improve the efficiency and quality of the information service of the digital library.

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Section: Text Classification Based On Deepmentioning

confidence: 99%

Digital Library Information Integration System Based on Big Data and Deep Learning

2022

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“…ese studies have taken into account both the whole and the parts, explored digital protection from various levels and angles, and laid a theoretical foundation for many future thematic studies [10]. Zhou et al believed that libraries and archives are important cultural protection and preservation centers, and the role they play in the digital protection of cultural heritage is not important.…”

Section: Literature Reviewmentioning

confidence: 99%

Digitally Protecting and Disseminating the Intangible Cultural Heritage in Information Technology Era

Liu

2022

Mobile Information Systems

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This paper studies the digital protection and dissemination of digital information based on intangible cultural heritage. The “digital era” is an important milestone, and there is no problem with the development of martial arts heritage. First, this paper explains that the development of the current martial arts heritage is not without drawbacks, for example, ecological marginalization and imperfect development. Then, the author of this study determined the benefits of combining incomplete martial arts technology with digital technology, and took the digital protection of Xingyi boxing as an example to explore its practice and specialize in digital protection. Then, the problems and procedures in digital protection are studied. Finally, with Xingyi boxing as an experiment, this form leads to separation. The experimental results show that, according to the full understanding of the status quo of nondigital defense culture, the protection measures to improve digital protection are put forward: determining the digital distribution process and writing models, completing knowledge presentation view, document creation, multimedia interactive platform development, and digital technology protection of public heritage, without any problems.

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Support Vector Regression Method for Regional Economic Mid‐ and Long‐Term Predictions Based on Wireless Network Communication

Dong

2021

Wireless Communications and Mobile Computing

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In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

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Integration of Museum User Behavior Information Based on Wireless Network

Cited by 3 publications

References 12 publications

Digital Library Information Integration System Based on Big Data and Deep Learning

Digital Library Information Integration System Based on Big Data and Deep Learning

Digitally Protecting and Disseminating the Intangible Cultural Heritage in Information Technology Era

Support Vector Regression Method for Regional Economic Mid‐ and Long‐Term Predictions Based on Wireless Network Communication

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