IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9899318.html
   My bibliography  Save this article

Fuzzy Applications in Demonstration of Chinese Teaching Research Based on Machine Learning

Author

Listed:
  • Lin Zhang
  • Wazid Michalak
  • Naeem Jan

Abstract

In order to improve the effect of Chinese teaching, this paper combines machine learning to conduct Chinese teaching research, constructs an intelligent Chinese teaching system, and conducts online monitoring of the Chinese teaching process. Moreover, this paper uses the general histogram matching algorithm to transfer the color style of the source image to the original image. The image’s local color information will be lost as a consequence of this procedure, according to the processing findings. This paper proposes a multi-directional cutting algorithm to partition the image, then uses the local linear embedding algorithm to realize the color style transfer method, and builds an intelligent Chinese teaching system based on machine learning to achieve a more realistic and natural color style transfer effect. The experimental research shows that the Chinese teaching method based on machine learning proposed in this paper can effectively improve the quality of Chinese teaching.

Suggested Citation

  • Lin Zhang & Wazid Michalak & Naeem Jan, 2022. "Fuzzy Applications in Demonstration of Chinese Teaching Research Based on Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, June.
    • Handle: RePEc:hin:jnlmpe:9899318
    DOI: 10.1155/2022/9899318
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/9899318.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/9899318.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9899318?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9899318. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.