JCTAM OPEN ACCESS

Journal of Computer Technology and Applied Mathematics

ISSN:3007-4126 (print) | ISSN:3007-4134 (online) | Publication Frequency: Bimonthly

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

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

10.70393/6a6374616d.323439
ark:/40704/JCTAM.v2n1a01
Authors: Wei Yang;Jincan Duan.
Abstract: 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...

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

10.70393/6a6374616d.323530
ark:/40704/JCTAM.v2n1a02
Authors: Qian Meng;Haoran Xu;Jingwen He.
Abstract: 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 Conc...

Innovative Applications of Machine Learning in Image Recognition

10.70393/6a6374616d.323533
ark:/40704/JCTAM.v2n1a03
Authors: Ke Qian.
Abstract: 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 ha...

Integrating Machine Learning for Optimal Path Planning

10.70393/6a6374616d.323534
ark:/40704/JCTAM.v2n1a04
Authors: Shiru Xiao.
Abstract: 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 ...

Machine Learning for Enhanced Classification and Geospatial Distribution Analysis

10.70393/6a6374616d.323535
ark:/40704/JCTAM.v2n1a05
Authors: Yuxi Huang.
Abstract: 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 intri...
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