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. 3 No. 1 (2026)
View Vol. 3 No. 1 (2026)

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: 9
Total number of pages in this issue: 78

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: 2026-01-05

Articles

  • Multi-Task Learning for Anomaly Detection and Remaining Useful Life Prediction in Semiconductor Manufacturing Systems
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Artificial Intelligence | Subjects: Machine Learning
    Publication ID: v3n1a01
    Abstract: In the current research on predictive maintenance for semiconductor manufacturing systems, although multi-task learning has shown promising results in certain applications, effectively integrating multiple tasks, such as anomaly detection and...
    1-10
    DOI Icon Abstract views: 61 | DOI Icon PDF downloads: 4 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/6a6374616d.333536
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a01
  • Bottleneck Diagnosis in International Automotive Sales Funnels Using Gradient Boosting Trees: Evidence from Cross-Regional Team Efficiency Evaluation
    Authors: Ziren Zhou
    Resource Type: Article
    Disciplines: Applied Mathematics | Subjects: Mathematical Modeling
    Publication ID: v3n1a02
    Abstract: Against the backdrop of slowing growth in the Chinese and global automotive markets, declining lead quantity and quality, fragmented online and offline data, and distorted data entry via manual DMS (Data Management System) have made it difficult...
    11-18
    DOI Icon Abstract views: 30 | DOI Icon PDF downloads: 14 | DOI Icon references: 10
    DOI Icon DOI: 10.70393/6a6374616d.333631
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a02
  • AI-Assisted UI Design: Enhancing Efficiency and Creativity through Generative Tools
    Authors: Lingxin Sun
    Resource Type: Article
    Disciplines: Computer Application Technology | Subjects: Human-Computer Interaction
    Publication ID: v3n1a03
    Abstract: The growing adoption of Artificial Intelligence (AI) in web-based products has made the design of user interfaces that effectively surface and control AI capabilities increasingly critical. In this context, understanding the key characteristics...
    19-27
    DOI Icon Abstract views: 55 | DOI Icon PDF downloads: 14 | DOI Icon references: 36
    DOI Icon DOI: 10.70393/6a6374616d.333638
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a03
  • Intelligent Operations and Public Relations Collaborative Management Model for Corporate Image Enhancement in the Smart Manufacturing Environment
    Authors: Wusheng Gao
    Resource Type: Article
    Disciplines: Computer Application Technology | Subjects: Human-Computer Interaction
    Publication ID: v3n1a04
    Abstract: The article focuses on intelligent operational relationships in an innovative manufacturing environment, primarily through the integration of PR strategy. A key focus is on enhancing SMEs' image. Using advanced technologies such as AIoT and...
    28-34
    DOI Icon Abstract views: 22 | DOI Icon PDF downloads: 8 | DOI Icon references: 12
    DOI Icon DOI: 10.70393/6a6374616d.333632
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a04
  • Human in the Loop AI EDA for Chip Design
    Authors: Lei Zhang
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n1a05
    Abstract: Currently, chip design automation is hampered by factors such as data scarcity, difficulty in sharing, and high costs associated with design rules and verification. Based on the research trajectory of human-machine collaborative learning, this...
    35-44
    DOI Icon Abstract views: 57 | DOI Icon PDF downloads: 22 | DOI Icon references: 28
    DOI Icon DOI: 10.70393/6a6374616d.333732
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a05
  • Industrial Policy for Semiconductors in the AI Hardware Era: Text Based Measurement and Cross Country Evidence
    Authors: Kai Ye
    Resource Type: Article
    Disciplines: Natural Language Processing | Subjects: Text Mining
    Publication ID: v3n1a06
    Abstract: As demand for AI hardware increases, semiconductor industry policies, through subsidies, tax credits, and targeted financing, are expanding, leading to uneven distribution of policy support documents and strategic frameworks, as well as long...
    45-54
    DOI Icon Abstract views: 19 | DOI Icon PDF downloads: 14 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/6a6374616d.333730
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a06
  • Edge-Enabled Real-Time Fraud Detection for Network Lending Terminals under Low-Latency Constraints
    Authors: Ximeng Yang, Yiming Zhang
    Resource Type: Article
    Disciplines: Network Technology | Subjects: Wireless Networks
    Publication ID: v3n1a07
    Abstract: This study proposes an adaptive edge–cloud collaborative framework for real-time fraud detection in network lending terminals, addressing the challenges posed by extreme class imbalance and latency constraints. Using the publicly available Credit...
    55-62
    DOI Icon Abstract views: 24 | DOI Icon PDF downloads: 9 | DOI Icon references: 15
    DOI Icon DOI: 10.70393/6a6374616d.333630
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a07
  • The Role of Financial Informatization in Enhancing Enterprise Risk Management
    Authors: Yinlei Chen
    Resource Type: Article
    Disciplines: Big Data Technology | Subjects: Data Analytics
    Publication ID: v3n1a08
    Abstract: In today's rapidly evolving business environment, the integration of financial information technology and enterprise risk management has become a crucial research area. This paper aims to explore the relationship between financial information...
    63-70
    DOI Icon Abstract views: 31 | DOI Icon PDF downloads: 12 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/6a6374616d.333633
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a08
  • Data-Driven Bottleneck Detection with Minimal Information in Manufacturing Systems
    Authors: Leede Frank
    Resource Type: Article
    Disciplines: Applied Mathematics | Subjects: Mathematical Modeling
    Publication ID: v3n1a09
    Abstract: Bottleneck detection is crucial for optimizing production systems, but many current methods in manufacturing environments rely on large amounts of process data that are difficult to obtain in real time. This paper explores a novel data-driven...
    71-78
    DOI Icon Abstract views: 66 | DOI Icon PDF downloads: 17 | DOI Icon references: 28
    DOI Icon DOI: 10.70393/6a6374616d.333634
    DOI Icon ARK: ark:/40704/JCTAM.v3n1a09
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