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: 3
Total number of pages in this issue: 17

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 14

Articles

Open Access Research ArticlePublished 14 January 2025

Enhancing Media Convergence with Artificial Intelligence to Stabilize Financial Markets

Haozhong Xue, Yanyi Zhong, Jingwen He

This study explores the application of artificial intelligence (AI) technology in media convergence, focusing on how AI is driving deep integration of media and financial markets through big data analytics, AIGC (AI-generated content), and intelligent communication technologies. Ai-driven sentiment analysis and fake news detection tools effectively solve the problem of information asymmetry and the spread of false news in the financial market and promote market stability and transparency. Through personalized recommendations and intelligent communication, AI provides users with a more accurate content experience and improves user engagement and satisfaction. In addition, the rapid development of AIGC and big data ecology has promoted the intellectualization of information dissemination and public opinion analysis, providing more forward-looking support for financial market decision-making.

Disciplines:Computer ScienceSubjects:Artificial IntelligenceReferences:32

DOI10.70393/616a6e73.323531ARKark:/40704/AJNS.v2n1a01
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Open Access Research ArticlePublished 14 January 2025

Integrating Stochastic Programming and Machine Learning for Enhanced Pre-disaster Relocation Planning

Yuqun Zhou, Zuen Cen

This paper proposes a novel framework that integrates stochastic programming and machine learning to optimize pre-disaster relocation strategies. Building upon existing game-theoretic and decision analysis models, this study presents a two-stage stochastic programming model coupled with predictive analytics to manage uncertainties associated with flooding risks and resident relocation behaviors. Machine learning algorithms, such as decision trees and gradient boosting, are employed to capture the variability in residents' decision-making, enhancing the precision of subsidy and policy impact forecasts. This combined approach offers governments innovative tools for implementing cost-effective, proactive relocation measures that mitigate long-term social and economic disruption. Additionally, by leveraging stochastic programming's robust handling of uncertainty and machine learning's data-driven insights, the framework ensures that relocation policies are both adaptive and equitable, addressing diverse community needs and long-term resilience planning.

Disciplines:Computer ScienceSubjects:Artificial IntelligenceReferences:27

DOI10.70393/616a6e73.323631ARKark:/40704/AJNS.v2n1a02
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Open Access Research ArticlePublished 14 January 2025

Harnessing Large Language Models and Stochastic Programming for Optimized Plant Breeding Strategies

Yuqun Zhou, Zuen Cen

The convergence of Generative AI (GenAI) and stochastic programming introduces unprecedented opportunities for optimizing plant breeding strategies under uncertainty. This paper presents a hybrid framework that integrates Large Language Models (LLMs) with stochastic programming to enhance decision-making in crop improvement. LLMs are employed to analyze vast datasets, generate insights on genotype-environment interactions, and simulate breeding scenarios, while stochastic programming optimizes the selection of genotypes for maximum yield and resilience. Case studies demonstrate the effectiveness of this approach in addressing challenges such as climate variability and evolving market demands, offering a transformative solution for sustainable agriculture.

Disciplines:Biological SciencesSubjects:GeneticsReferences:28

DOI10.70393/616a6e73.323632ARKark:/40704/AJNS.v2n1a03
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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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Academic Journal of Natural Science (AJNS)
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