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: 3
Total number of pages in this issue: 26

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: 2025-12-03

Articles

  • Defect Prediction and Optimization in Semiconductor Manufacturing Using Explainable AutoML
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Artificial Intelligence
    Publication ID: v2n4a01
    Abstract: The semiconductor manufacturing industry often faces the severe challenges of data scarcity and imbalance. While the semiconductor industry has conducted extensive research on leveraging machine learning to improve yield, defect prediction remains...
    1-10
    DOI Icon Abstract views: 26 | DOI Icon PDF downloads: 11 | DOI Icon references: 29
    DOI Icon DOI: 10.70393/616a6e73.333533
    DOI Icon ARK: ark:/40704/AJNS.v2n4a01
  • A Comparative Study of Machine Learning Models for Credit Card Fraud Detection
    Authors: Yinlei Chen
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v2n4a02
    Abstract: Currently, credit card fraud detection is a unique problem in the financial sector, with both institutions and consumers facing increasingly significant losses. Despite the growing application of machine learning (ML) techniques in this domain,...
    11-18
    DOI Icon Abstract views: 27 | DOI Icon PDF downloads: 9 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/616a6e73.333530
    DOI Icon ARK: ark:/40704/AJNS.v2n4a02
  • A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Data Science
    Publication ID: v2n4a03
    Abstract: With the rapid development of the semiconductor industry, identifying and optimizing bottlenecks is crucial for improving production line efficiency. This paper proposes a method combining Activity Cycle Method (APM) and data visualization...
    19-26
    DOI Icon Abstract views: 34 | DOI Icon PDF downloads: 7 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/616a6e73.333534
    DOI Icon ARK: ark:/40704/AJNS.v2n4a03