Optimizing Supply Chain Demand Forecasting and Inventory Management Using Large Language Models

Authors

  • Tianyu Lu Computer Science, Northeastern University, MA, USA
  • Emily Garcia Business Administration California State University, Los Angeles, CSULA, USA
  • Jackson Lee Computer Technology, Loyola Marymount University, LMU, USA

Keywords:

Machine learning; Computer Vision; Internet of Things Optimization; American National Security

Abstract

This paper explores the potential for optimizing the Internet of Things by integrating machine learning and computer vision technologies and its implications for U.S. national security and economic competitiveness. First, the application of machine learning in IoT device optimization is introduced, emphasizing its ability to improve system intelligence and efficiency. Secondly, the critical role of computer vision technology in monitoring and reacting to changes in the physical environment is discussed, especially in security applications, such as the protection of national infrastructure and border security. Finally, the strategic significance of integrating these technologies in national security strategy and economic development is analyzed, and the direction and challenge of future research are put forward.

References

L. Li, Y. Zhang, J. Wang, and X. Ke, "Deep learning-based network traffic anomaly detection: A study in IoT environments," 2024. Available from: https://doi.org/10.53469/wjimt.2024.07(06).03

G. Cao, Y. Zhang, Q. Lou, and G. Wang, "Optimization of high-frequency trading strategies using deep reinforcement learning," Journal of Artificial Intelligence General Science (JAIGS), vol. 6, no. 1, pp. 230–257, 2024, ISSN: 3006-4023. Available from: https://doi.org/10.60087/jaigs.v6i1.247

G. Wang, X. Ni, Q. Shen, and M. Yang, "Leveraging large language models for context-aware product discovery in e-commerce search systems," Journal of Knowledge Learning and Science Technology, vol. 3, no. 4, 2024, ISSN: 2959-6386 (online). Available from: https://doi.org/10.60087/jklst.v3.n4.p300

H. Zhang, et al., "Enhancing facial micro-expression recognition in low-light conditions using attention-guided deep learning," Journal of Economic Theory and Business Management, vol. 1, no. 5, pp. 12–22, 2024. Available from: https://doi.org/10.5281/zenodo.13933725

J. Wang, T. Lu, L. Li, and D. Huang, "Enhancing personalized search with AI: A hybrid approach integrating deep learning and cloud computing," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 127–138, 2024. Available from: https://doi.org/10.5281/zenodo.13998900

S. Zhou, W. Zheng, Y. Xu, and Y. Liu, "Enhancing user experience in VR environments through AI-driven adaptive UI design," Journal of Artificial Intelligence General Science (JAIGS), vol. 6, no. 1, pp. 59–82, 2024. Available from: https://doi.org/10.60087/jaigs.v6i1.230

M. Yang, D. Huang, H. Zhang, and W. Zheng, "AI-enabled precision medicine: Optimizing treatment strategies through genomic data analysis," Journal of Computer Technology and Applied Mathematics, vol. 1, no. 3, pp. 73–84, 2024. Available from: https://doi.org/10.5281/zenodo.13380619

X. Wen, Q. Shen, W. Zheng, and H. Zhang, "AI-driven solar energy generation and smart grid integration: A holistic approach to enhancing renewable energy efficiency," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 4, pp. 55–66, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.4.8

S. Zhou, B. Yuan, K. Xu, M. Zhang, and W. Zheng, "The impact of pricing schemes on cloud computing and distributed systems," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 193–205, 2024. Available from: https://doi.org/10.60087/jklst.v3.n3.p206-224

Y. Zhang, W. Bi, and R. Song, "Research on deep learning-based authentication methods for e-signature verification in financial documents," Academic Journal of Sociology and Management, vol. 2, no. 6, pp. 35–43, 2024. Available from: https://doi.org/10.5281/zenodo.14161744

Z. Zhou, S. Xia, M. Shu, and H. Zhou, "Fine-grained abnormality detection and natural language description of medical CT images using large language models," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 6, pp. 52–62, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.6.8

Y. Zhang, Y. Liu, and S. Zheng, "A graph neural network-based approach for detecting fraudulent small-value high-frequency accounting transactions," Academic Journal of Sociology and Management, vol. 2, no. 6, pp. 25–34, 2024. Available from: https://doi.org/10.5281/zenodo.14161459

S. Huang, Y. Liang, F. Shen, and F. Gao, "Research on federated learning's contribution to trustworthy and responsible artificial intelligence," in Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering, July 2024, pp. 125–129. Available from: https://doi.org/10.1145/3689299.3689322

K. Yu, Q. Shen, Q. Lou, Y. Zhang, and X. Ni, "A deep reinforcement learning approach to enhancing liquidity in the US municipal bond market: An intelligent agent-based trading system," International Journal of Engineering and Management Research, vol. 14, no. 5, pp. 113–126, 2024. Available from: https://doi.org/10.5281/zenodo.14184756

Y. Wang, Y. Zhou, H. Ji, Z. He, and X. Shen, "Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data," in 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), IEEE, March 2024, pp. 1425–1429. Available from: https://doi.org/10.1109/ICAACE61206.2024.10548953

Akbar, N. Peoples, H. Xie, P. Sergot, H. Hussein, W. F. Peacock IV, and Z. Rafique, "Thrombolytic administration for acute ischemic stroke: What processes can be optimized?" McGill Journal of Medicine, vol. 20, no. 2, 2022. Available from: https://doi.org/10.26443/mjm.v20i2.881

Z. Feng, M. Ge, and Q. Meng, "Enhancing energy efficiency in green buildings through artificial intelligence," Applied Science and Engineering Journal for Advanced Research, vol. 3, no. 5, pp. 10–17, 2024. Available from: https://www.preprints.org/manuscript/202408.1489

J. Chen, J. Xiao, and W. Xu, "A hybrid stacking method for short-term price forecasting in electricity trading market," in 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, 2024, pp. 1–5. Available from: https://doi.org/10.1109/ICITISEE63424.2024.10730623

Y. Zhang, H. Xie, S. Zhuang, and X. Zhan, "Image processing and optimization using deep learning-based generative adversarial networks (GANs)," Journal of Artificial Intelligence General Science (JAIGS), vol. 5, no. 1, pp. 50–62, 2024. Available from: https://doi.org/10.60087/jaigs.v5i1.163

T. Lu, M. Jin, M. Yang, and D. Huang, "Deep learning-based prediction of critical parameters in CHO cell culture process and its application in monoclonal antibody production," International Journal of Advance in Applied Science Research, vol. 3, pp. 108–123, 2024. Available from: https://h-tsp.com/index.php/ijaasr/article/view/69

S. Xia, Y. Zhu, S. Zheng, T. Lu, and X. Ke, "A deep learning-based model for P2P microloan default risk prediction," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 5, pp. 110–120, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.5.16

J. Xiao, T. Deng, and S. Bi, "Comparative analysis of LSTM, GRU, and transformer models for stock price prediction," arXiv preprint, arXiv:2411.05790, 2024. Available from: https://doi.org/10.48550/arXiv.2411.05790

W. Zheng, M. Yang, D. Huang, and M. Jin, "A deep learning approach for optimizing monoclonal antibody production process parameters," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 6, pp. 18–29, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.6.4

X. Ma, J. Wang, X. Ni, and J. Shi, "Machine learning approaches for enhancing customer retention and sales forecasting in the biopharmaceutical industry: A case study," International Journal of Engineering and Management Research, vol. 14, no. 5, pp. 58–75, 2024. Available from: https://doi.org/10.5281/zenodo.14053620

S. Huang, S. Diao, H. Zhao, and L. Xu, f federated learning to AI development," in The 24th International Scientific and Practical Conference 'Technologies of Scientists and Implementation of Modern Methods', June 2024, pp. 358. Available from: http://dx.doi.org/10.20944/preprints202407.0551.v1

H. Zheng, J. Wu, R. Song, L. Guo, and Z. Xu, "Predicting financial enterprise stocks and economic data trends using machine learning time series analysis," Applied and Computational Engineering, vol. 87, pp. 26–32, 2024. Available from: https://www.preprints.org/manuscript/202407.0895

H. Zheng, K. Xu, M. Zhang, H. Tan, and H. Li, "Efficient resource allocation in cloud computing environments using AI-driven predictive analytics," Applied and Computational Engineering, vol. 82, pp. 6–12, 2024. Available from: https://doi.org/10.54254/2755-2721/82/2024GLG0055

B. Wang, H. Zheng, K. Qian, X. Zhan, and J. Wang, "Edge computing and AI-driven intelligent traffic monitoring and optimization," Applied and Computational Engineering, vol. 77, pp. 225–230, 2024. Available from: https://doi.org/10.54254/2755-2721/77/2024MA0062

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Published

2024-11-28

How to Cite

[1]
Tianyu Lu, Emily Garcia, and Jackson Lee, “Optimizing Supply Chain Demand Forecasting and Inventory Management Using Large Language Models”, IJIRCST, vol. 12, no. 6, pp. 89–94, Nov. 2024.

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