About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
el 23(1):

Editorial

Blended Learning for Machine Learning-based Image Classification

Download92 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetel.4509,
        author={Shengpei Ye},
        title={Blended Learning for Machine Learning-based Image Classification},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={EL},
        year={2023},
        month={12},
        keywords={blending learning, image classification, machine learning},
        doi={10.4108/eetel.4509}
    }
    
  • Shengpei Ye
    Year: 2023
    Blended Learning for Machine Learning-based Image Classification
    EL
    EAI
    DOI: 10.4108/eetel.4509
Shengpei Ye1,*
  • 1: Henan Polytechnic University
*Contact email: YeShengpei@home.hpu.edu.cn

Abstract

The paper commences with an introduction to blended learning, an educational approach that amalgamates traditional face-to-face instruction with online learning, aiming to capitalize on the advantages of conventional classroom instruction and digital resources in order to enhance the overall learning experience. The incorporation of diverse technologies facilitates a personalized learning experience that caters to the needs and learning styles of individual students. Image classification entails training machine learning models to categorize or label images into predetermined classes or categories, empowering machines to recognize and comprehend crucial components of visual information, emulating humans' classification of objects in the real world. The crux of image classification relies on extracting meaningful features from images and distinguishing different categories by associating specific features with distinct classes through iterative optimization learning. Machine learning significantly aids image classification by endowing automated systems with the capability to discern patterns, features, and distinctions within datasets, ultimately achieving accurate image classification. The integration of hybrid learning methods can augment the training process for machine learning models used in image classification by providing a flexible and adaptive learning environment.

Keywords
blending learning, image classification, machine learning
Received
2023-11-30
Accepted
2023-12-07
Published
2023-12-11
Publisher
EAI
http://dx.doi.org/10.4108/eetel.4509

Copyright © 2023 S. Ye et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL