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. 4 (2025)
View Vol. 2 No. 4 (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: 11

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: 2025-08-24

Articles

  • Application of Machine Learning-based Customer Flow Pattern Analysis in Restaurant Seating Layout Design
    Authors: Sheng Xu, Le Yu
    Resource Type: Article
    Disciplines: Artificial Intelligence | Subjects: Machine Learning
    Publication ID: v2n4a01
    Abstract: Contemporary restaurant design faces unprecedented challenges in optimizing spatial efficiency while enhancing customer experience. Traditional seating layout methodologies rely primarily on empirical knowledge and static design principles, often...
    1-11
    DOI Icon Abstract views: 15 | DOI Icon PDF downloads: 6 | DOI Icon references: 59
    DOI Icon DOI: 10.70393/6a6374616d.333135
    DOI Icon ARK: ark:/40704/JCTAM.v2n4a01
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