The Application of Artificial Intelligence and Big Data in Computer Networks
Keywords:
Artificial intelligence and big data, Computer network, ApplicationAbstract
The application of artificial intelligence and big data in computer network technology is becoming increasingly widespread. The vast, diverse, and rapidly growing characteristics of big data, combined with the intelligent processing capabilities of artificial intelligence, not only improve data processing efficiency and accuracy, but also significantly enhance network security and stability. Artificial intelligence has achieved intelligent monitoring, anomaly detection, and real-time protection of network traffic through optimized algorithms, while promoting the development of personalized services and precision marketing. The combination of the two has promoted the intelligent upgrading of computer network technology and provided strong support for the digital transformation of various industries.
References
Tang, Z., Feng, Y., Zhang, J., & Wang, Z. (2026). SVD-BDRL: A trustworthy autonomous driving decision framework based on sparse voxels and blockchain enhancement. Alexandria Engineering Journal, 134, 433-446.
Lu, K., Sui, Q., Chen, X., & Wang, Z. (2025). NeuroDiff3D: a 3D generation method optimizing viewpoint consistency through diffusion modeling. Scientific Reports, 15(1), 41084.
Zhang, T. (2025). A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement. Frontiers in Business and Finance, 2(02), 247-261.
Xie, J., Zhang, L., Cheng, L., Yao, J., Qian, P., Zhu, B., & Liu, G. (2025). MARNet: Multi-scale adaptive relational network for robust point cloud completion via cross-modal fusion. Information Fusion, 103505.
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Sultan, N., Patwar, N., Wei, X., Chew, J., Liu, J., & Du, R. (2026). FedGuard: A Robust Federated AI Framework for Privacy-Conscious Collaborative AML, Inspired by DARPA GARD Principles. International Academic Journal of Social Science, 2, 1–16. https://doi.org/10.5281/zenodo.18253151
Zhu, Y., Yu, W., & Li, R. (2025). SAGCN: A spatiotemporal attention-weighted graph convolutional network with IoT integration for adolescent tennis motion analysis. Alexandria Engineering Journal, 128, 652-662.
Zhang, X. (2024). Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering. Innovation & Technology Advances, 2(2), 59–75. https://doi.org/10.61187/ita.v2i2.135
Yang, J., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
We, X., Lin, S., Pruś, K., Zhu, X., Jia, X., & Du, R. (2025). Towards Intelligent Monitoring of Anesthesia Depth by Leveraging Multimodal Physiological Data. International Journal of Advance in Clinical Science Research, 4, 26–37. Retrieved from https://www.h-tsp.com/index.php/ijacsr/article/view/158
Yang, Y. (2025). Research on Site Reliability Optimization Technology Based on Synthetic Monitoring in Cloud Environments.
Guo, Y., & Tao, D. (2025). Modeling and Simulation Analysis of Robot Environmental Interaction. Artificial Intelligence Technology Research, 2(8).
Peng, Qucheng, et al. "RAIN: regularization on input and network for black-box domain adaptation." Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. 2023.
Xie, Minhui, and Shujian Chen. "Maestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines." Authorea Preprints (2025).
Qin, Haoshen, et al. "Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression." Digital health 11 (2025): 20552076251349719.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jiahe Guo

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
