IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01058-2.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01058-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-021-01058-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. San Martín, Jesús & Drubi, Fátima & Rodríguez Pérez, Daniel, 2020. "Uncritical polarized groups: The impact of spreading fake news as fact in social networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 192-206.
    2. Liu, Zhi-Jiang & Chernov, Sergei & Mikhaylova, Anna V., 2021. "Trust management and benefits of vehicular social networking: An approach to verification and safety," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
    4. Pauline Ong & Choon Sin Ho & Desmond Daniel Vui Sheng Chin & Chee Kiong Sia & Chuan Huat Ng & Md Saidin Wahab & Abduladim Salem Bala, 2019. "Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1957-1972, April.
    5. Woong-Gi Kim & Namhyuk Ham & Jae-Jun Kim, 2021. "Enhanced Subcontractors Allocation for Apartment Construction Project Applying Conceptual 4D Digital Twin Framework," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    6. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    7. Antonio Armillotta, 2021. "On the role of complexity in machining time estimation," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2281-2299, December.
    8. Yu, Hongxin & Zhao, Yuanjun & Liu, Zheng & Liu, Wei & Zhang, Shuai & Wang, Fatao & Shi, Lihua, 2021. "Research on the financing income of supply chains based on an E-commerce platform," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    9. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    10. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    11. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01058-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.