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Developing the network social media in graphic design based on artificial neural network

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  • Yaxuan Liu

    (Luxun Academy of Fine Arts)

Abstract

The purposes are to effectively solve the graphic design problems, develop an easy-to-use supporting design program, and make graphic design more reliable and accurate. Based on the analysis of the current graphic design framework, graphic design data are obtained from the network social media. A system of surrounding rock classification and support optimization design is developed by a deep neural network structure. The model’s effectiveness is verified by more than 3000 road conditions data. The results show that the three-layer network’s errors are 0.0062 with a training time of 12,455, and the five-layer network’s errors are 0.00019 with a training time of 69,895. With the input layer, hidden layer, and the output layer of 8, 15, and 5 respectively, the model performs best. In the deep learning algorithm, the deep backpropagation neural network (Deep BPNN) can obtain the best training effects with less training time. Therefore, the roadway drawing system’s application based on the deep learning algorithm to the roadway support design can improve design efficiency and scientificity.

Suggested Citation

  • Yaxuan Liu, 2021. "Developing the network social media in graphic design based on artificial neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 640-653, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01058-2
    DOI: 10.1007/s13198-021-01058-2
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    References listed on IDEAS

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    1. Cheng Li & Zhiyong Zhang & Lanfang Zhang, 2018. "A Novel Authorization Scheme for Multimedia Social Networks Under Cloud Storage Method by Using MA-CP-ABE," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 8(3), pages 32-47, July.
    2. Hao Ding & Xinghong Jiang & Ke Li & Hongyan Guo & Wenfeng Li, 2020. "Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, November.
    3. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    4. Sangeeta Gupta & Narsimha Gugulothu, 2018. "Secure NoSQL for the Social Networking and E-Commerce Based Bigdata Applications Deployed in Cloud," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 8(2), pages 113-129, April.
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