About the Journal

Journal of Computer Technology and Applied Mathematics is peer-reviewed journal of Southern United Academy of Sciences published in the field of Applied Mathematics, Computer Science and Technology. The journal registers independent DOI and ARK for each article. The DOI prefix is ​​10.70393 and the ARK prefix is ​​40704.

ISSN 3007-4126 (Print), ISSN 3007-4134 (Online), ISSN 3007-4142 (Digital), International CODEN (CAS): JCTAAD

The topics related to this journal include:

  • Applied Mathematics - Computational Mathematics, Mathematical Modeling, Numerical Analysis
  • Statistical Analysis - Probability Theory, Data Mining, Bayesian Statistics
  • Geometric Theory - Algebraic Geometry, Differential Geometry, Topology
  • Network Technology - Wireless Networks, Network Security, Internet of Things (IoT)
  • Artificial Intelligence and Intelligence - Machine Learning, Deep Learning, Neural Networks
  • Big Data Technology - Data Warehousing, Data Analytics, Distributed Computing
  • Computer Vision - Image Recognition, Object Detection, Motion Analysis
  • Natural Language Processing - Text Mining, Speech Recognition, Machine Translation
  • Computer Application Technology - Human-Computer Interaction, Embedded Systems, Mobile Computing
  • Software Technology - Software Engineering, Software Testing, Software Architecture

All articles published by JCTAM are indexed by OpenAIRE, BASE, WorldCat and ICI.

(OpenAIRE - DataSources | BASE - Bielefeld Academic Search Engine | WorldCat - OCLC | ICI - European Funds)

All articles published are rigorously reviewed meeting the Journal Quality standards.

Announcements

Current Issue

Vol. 2 No. 5 (2025)
					View Vol. 2 No. 5 (2025)

All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.

Total number of articles in this issue: 1
Total number of pages in this issue: 12

For inquiries regarding the content of specific articles, please feel free to contact the respective authors via their provided email addresses. For questions related to the journal itself, please reach out directly to SUAS Press.

Published: 2025-09-13

Articles

  • Authors: Guifan Weng, Wenyan Liu, Lingfeng Guo
    Resource Type: Article
    Disciplines: Computer Vision | Subjects: Image Recognition
    Publication ID: v2n5a01
    Abstract: Corn leaf disease recognition represents a critical challenge in modern agricultural systems, where early detection can significantly impact crop yield and food security. This research investigates the application of advanced image enhancement...
    1-12
    DOI Icon Abstract views: 13 | DOI Icon PDF downloads: 3 | DOI Icon references: 45
    DOI Icon DOI: 10.70393/6a6374616d.333136
    DOI Icon ARK: ark:/40704/JCTAM.v2n5a01
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