Current Issue

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: 5
Total number of pages in this issue: 32

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.

Year

2025

Volume

2

Number

1

Status

Archived

Published

2025 January 1

Articles

Open Access Research ArticlePublished 1 January 2025

Knowledge Graph Construction for the U.S. Stock Market: A Statistical Learning and Risk Management Approach

Wei Yang, Jincan Duan

This paper explores the integration of dynamic knowledge graphs (DKGs) and advanced AI techniques, such as large language models (LLMs) and graph neural networks (GNNs), for enhancing financial market analysis and risk management. By developing the Integrated Context Knowledge Graph Generator (ICKG) and the Financial Dynamic Knowledge Graph (FinDKG), the study demonstrates how these models can predict market trends, optimize investment strategies, and improve risk mitigation. The results highlight the superior performance of the KGTransformer model in link prediction tasks, showcasing its potential for more accurate and insightful financial decision-making.

Disciplines:Artificial IntelligenceSubjects:Statistical AnalysisReferences:39

DOI10.70393/6a6374616d.323439ARKark:/40704/JCTAM.v2n1a01
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Open Access Research ArticlePublished 1 January 2025

Using Machine Learning for Sustainable Concrete Material Selection and Optimization in Building Design

Qian Meng, Haoran Xu, Jingwen He

This paper explores the application of machine learning (ML) in the selection and optimization of concrete materials for sustainable building design. It discusses how AI-driven platforms, such as Concrete Copilot and SmartMix, are revolutionizing concrete mix design by improving performance, reducing costs, and minimizing environmental impact. By leveraging ML techniques, these platforms enable real-time optimization of concrete ingredients, enhancing both resource efficiency and sustainability. The paper highlights the potential of machine learning to drive innovation in the concrete industry, contributing to the development of greener, more efficient building materials for future construction projects.

Disciplines:Machine LearningSubjects:Computer Application TechnologyReferences:35

DOI10.70393/6a6374616d.323530ARKark:/40704/JCTAM.v2n1a02
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Open Access Research ArticlePublished 1 January 2025

Innovative Applications of Machine Learning in Image Recognition

Ke Qian

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. This paper aims to devle into the scope of applications of Machine Learning in image recgonition.

Disciplines:Artificial Intelligence and IntelligenceSubjects:Machine LearningReferences:18

DOI10.70393/6a6374616d.323533ARKark:/40704/JCTAM.v2n1a03
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Open Access Research ArticlePublished 1 January 2025

Integrating Machine Learning for Optimal Path Planning

Shiru Xiao

In the area of AI based path planning, the learner is not told which actions to take, as is common in most forms of machine learning. Instead, the learner must discover through trial and error, which actions yield the most rewards. In the most interesting and challenging cases, actions affect not only the immediate rewards but also the next station or subsequent rewards. The characteristics of trial and error searches and delayed reward are two important distinguishing features of RL, which are defined not by characterizing learning methods, but by characterizing a learning problem.

Disciplines:Artificial IntelligenceSubjects:Machine LearningReferences:22

DOI10.70393/6a6374616d.323534ARKark:/40704/JCTAM.v2n1a04
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Open Access Research ArticlePublished 1 January 2025

Machine Learning for Enhanced Classification and Geospatial Distribution Analysis

Yuxi Huang

Combining geospatial analysis with machine learning creates a novel synergy beyond conventional approaches to comprehending our spatial surroundings. The ability of machine learning to recognize intricate patterns and connections within data has made it an indispensable instrument for geospatial analysts. This integration makes complex analyses of satellite imagery, climatic data, and geographic data possible, providing previously complex insights to obtain via manual or rule-based methods.

Disciplines:Artificial IntelligenceSubjects:Machine LearningReferences:23

DOI10.70393/6a6374616d.323535ARKark:/40704/JCTAM.v2n1a05
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Journal of Computer Technology and Applied Mathematics (JCTAM)
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