Application of Adaptive Machine Learning Systems in Heterogeneous Data Environments


  • Xubo Wu Independent Researcher, USA Author
  • Ying Wu University Maine Presque Isle, USA Author
  • Xintao Li University of Miami, USA Author
  • Zhi Ye Elevance Health USA Author
  • Xingxin Gu Northeastern University, USA Author
  • Zhizhong Wu Google LLC, USA Author
  • Yuanfang Yang Southern Methodist University, USA Author



Adaptive Machine Learning Systems, Heterogeneous Data Environments, Data Quality, Data Integration, Deep Learning, Generalization Ability


This paper explores the application and effectiveness of adaptive machine learning systems in heterogeneous data environments. With the diversification of data sources and types, traditional machine learning systems face numerous challenges, especially in data processing and model adaptability. Adaptive machine learning technologies optimize the capability to handle multi-source heterogeneous data by dynamically adjusting learning algorithms and model parameters, enhancing model accuracy and robustness. Research through theoretical analysis and multiple experiments demonstrates the effectiveness of adaptive systems in various application fields such as healthcare and finance, highlighting their advantages in complex data scenarios such as high noise and missing data. Future research will focus on improving model interpretability, optimizing large-scale data processing capabilities, expanding cross-domain applications, and strengthening data security and privacy protection to promote the widespread application and development of adaptive machine learning technology.


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Liang Min. Machine learning prediction models based on heterogeneous temporal data in electronic health records [D]. Advisor: Mo Yuchang. Huaqiao University, 2021.

Wang Yuqin. Application of machine learning in the analysis of blood disease data [D]. Advisors: Liu Li; Sun Sanshan. Sichuan Normal University, 2022.

Zhang Xiaoqiang, Jiang Jian, He Wenxiu, Zhu Chaoming, Zhao Yongbiao. Cross-domain adaptive mobile environment monitoring system based on machine learning [J]. Journal of Sensor Technology, 2023, 36(06):999-1004.

Sun Xiran. Application of machine learning classification algorithms in community question-and-answer systems [J]. Computer Knowledge and Technology, 2021, 17(12):195-197.

Wei Juhong, Chang Rundong. Application of machine learning in ecological environment big data [J]. Modern Industrial Economy and Informationization, 2022, 12(11):129-131.

Liang Ni, Han Lei. Application of adaptive control algorithms in industrial robot systems [J]. Electronic Technology, 2023, 52(10):158-159.

Kang Miaojian. Application of natural semantic analysis and machine learning in big data security [J]. Electronic Technology and Software Engineering, 2022, (18):202-207.

Zhao, Yu, and Haoxiang Gao. "Utilizing large language models for information extraction from real estate transactions." arXiv preprint arXiv:2404.18043 (2024).

Yang, Shiqi, Yu Zhao, and Haoxiang Gao. "Using Large Language Models in Real Estate Transactions: A Few-shot Learning Approach."

Zhao, Yu, Shiqi Yang, and Haoxiang Gao. "Utilizing Large Language Models to Analyze Common Law Contract Formation."

Li, Zhengning, et al. "High-Precision Neuronal Segmentation: An Ensemble of YOLOX, Mask R-CNN, and UPerNet." Journal of Theory and Practice of Engineering Science 4.04 (2024): 45-52.

Weng, Yijie, and Jianhao Wu. "Fortifying the global data fortress: a multidimensional examination of cyber security indexes and data protection measures across 193 nations." International Journal of Frontiers in Engineering Technology 6.2 (2024): 13-28.

Weng, Yijie. "Big data and machine learning in defence." International Journal of Computer Science and Information Technology 16.2 (2024): 25-35.

Lai, Yingxin, Zhiming Luo, and Zitong Yu. "Detect any deepfakes: Segment anything meets face forgery detection and localization." Chinese Conference on Biometric Recognition. Singapore: Springer Nature Singapore, 2023.

Lai, Yingxin, et al. "Selective Domain-Invariant Feature for Generalizable Deepfake Detection." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.

Liao, Shan, et al. "Measuring complex permittivity of soils by waveguide transmission/reflection method." IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019.

Lang, R., et al. "Measurement of Dielectric Constant of Seawater at P Band." IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023.

Lang, Roger, et al. "A cavity system for seawater dielectric measurements at P-band." IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022.

Li, Ming, et al. "Impact of flat subsurface approximation on scattering of multilayer media." Waves in Random and Complex Media 32.2 (2022): 641-662.

Li, Ming, et al. "Scattering from fractal surfaces based on decomposition and reconstruction theorem." IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-12.

Zhou, Qiqin. "Application of Black-Litterman Bayesian in Statistical Arbitrage." arXiv preprint arXiv:2406.06706 (2024).

Zhou, Qiqin. "Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints." arXiv preprint arXiv:2406.00610 (2024).

Peng, Hongwu, et al. "MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training." Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. 2024.

Xie, Xi, et al. "Accel-gcn: High-performance gpu accelerator design for graph convolution networks." 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). IEEE, 2023.

Peng, Hongwu, et al. "Autorep: Automatic relu replacement for fast private network inference." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.

Jin, Can, et al. "Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate." arXiv preprint arXiv:2402.02769 (2024).

Jin, Can, et al. "Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective." arXiv preprint arXiv:2312.01397 (2023).

Peng, Hongwu, et al. "Lingcn: Structural linearized graph convolutional network for homomorphically encrypted inference." Advances in Neural Information Processing Systems 36 (2024).

Zhu, Armando, et al. "Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification." Journal of Computer Technology and Applied Mathematics 1.1 (2024): 46-53.

Li, Keqin, et al. "Utilizing deep learning to optimize software development processes." arXiv preprint arXiv:2404.13630 (2024).

Li, Keqin, et al. "The application of augmented reality (ar) in remote work and education." arXiv preprint arXiv:2404.10579 (2024).

Zhu, Armando, et al. "Exploiting Diffusion Prior for Out-of-Distribution Detection." arXiv preprint arXiv:2406.11105 (2024).

Hong, Bo, et al. "The application of artificial intelligence technology in assembly techniques within the industrial sector." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 1-12.

Dai, Shuying, et al. "AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 13-21.

Zhao, Peng, et al. "Task allocation planning based on hierarchical task network for national economic mobilization." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 22-31.

Zhao, Haopeng, et al. "Optimization Strategies for Self-Supervised Learning in the Use of Unlabeled Data." Journal of Theory and Practice of Engineering Science 4.05 (2024): 30-39.

Peng, Xirui, et al. "Automatic News Generation and Fact-Checking System Based on Language Processing." arXiv preprint arXiv:2405.10492 (2024).

Shen, Xinyu, et al. "Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data." ResearchGate, May (2024).







How to Cite

Wu, X., Wu, Y., Li, X., Ye, Z., Gu, X., Wu, Z. ., & Yang, Y. . (2024). Application of Adaptive Machine Learning Systems in Heterogeneous Data Environments. Global Academic Frontiers, 2(3), 37-50.