Vol. 3 No. 6 (2025)

View Vol. 3 No. 6 (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: 6
Total number of pages in this issue: 46

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

  • Development of AI Multi-Agent Frameworks for Financial Services
    Authors: Chen Li
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n6a01
    Abstract: Large-language-model-based multi-agent architectures and distributed AI components are rapidly reshaping financial services. They enable autonomous decision-making, collaboration in problem-solving, and automation of complex workflows across risk...
    1-5
    DOI Icon Abstract views: 59 | DOI Icon PDF downloads: 20 | DOI Icon references: 11
    DOI Icon DOI: 10.70393/6a69656173.333337
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a01
  • Research on an Automated Data Insight Generation Method Based on Large Language Models
    Authors: Jingtao Hong, Huichen Ma
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Natural Language Processing
    Publication ID: v3n6a02
    Abstract: This study aims to explore automated data insight generation methods based on large language models (LLMs), and systematically analyzes the application potential and challenges of LLMs in the field of data insights. Starting from an overview of...
    6-12
    DOI Icon Abstract views: 35 | DOI Icon PDF downloads: 19 | DOI Icon references: 18
    DOI Icon DOI: 10.70393/6a69656173.333436
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a02
  • Data Quality Control in Semiconductor Manufacturing through Automated ETL Processes and Class Imbalance Handling Techniques
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Information Science | Subjects: Data Management
    Publication ID: v3n6a03
    Abstract: In semiconductor manufacturing, ensuring data quality is crucial for maintaining high production efficiency and product consistency. However, missing values, noise, and class imbalance in sensor data complicate the quality control process. This...
    13-22
    DOI Icon Abstract views: 44 | DOI Icon PDF downloads: 23 | DOI Icon references: 26
    DOI Icon DOI: 10.70393/6a69656173.333532
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a03
  • Generative Diffusion Models for Option Pricing: A Novel Framework for Modeling Volatility Dynamics in U.S. Financial Markets
    Authors: Yinlei Chen
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n6a04
    Abstract: This study proposes a generative diffusion modeling framework to estimate option prices and volatility surfaces in U.S. financial markets. Unlike conventional stochastic volatility models, the diffusion model learns the data-generating process...
    23-29
    DOI Icon Abstract views: 57 | DOI Icon PDF downloads: 25 | DOI Icon references: 35
    DOI Icon DOI: 10.70393/6a69656173.333338
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a04
  • Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises
    Authors: Wenwen Liu
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n6a05
    Abstract: To enhance the execution of growth strategies for SMEs under data sparsity and domain shift, this study combines domain adaptation with few-shot learning to identify growth bottlenecks and generate actionable strategies through model optimization....
    30-35
    DOI Icon Abstract views: 36 | DOI Icon PDF downloads: 20 | DOI Icon references: 5
    DOI Icon DOI: 10.70393/6a69656173.333434
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a05
  • Predictive Maintenance of Semiconductor Equipment Using Stacking Classifiers and Explainable AI: A Synthetic Data Approach for Fault Detection and Severity Classification
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n6a06
    Abstract: In the semiconductor manufacturing industry, predictive maintenance is a key strategy for reducing equipment downtime. This paper proposes a machine learning-based predictive model for semiconductor equipment operating conditions, employing a...
    36-46
    DOI Icon Abstract views: 69 | DOI Icon PDF downloads: 23 | DOI Icon references: 28
    DOI Icon DOI: 10.70393/6a69656173.333439
    DOI Icon ARK: ark:/40704/JIEAS.v3n6a06