AI-Powered Real-Time Communication in Software-Defined Networks: Machine Learning–Driven Optimization for Risk Analytics and Medical Imaging on Oracle Cloud
DOI:
https://doi.org/10.15662/IJARCST.2024.0706017Keywords:
Software-Defined Networking (SDN), Artificial Intelligence (AI), Machine Learning (ML), Real-Time Communication, Risk Analytics, Medical Imaging, Oracle Cloud Infrastructure (OCI), Deep Learning, Network Optimization, Cybersecurity, Edge Computing, Cloud-Based Healthcare Systems.Abstract
The rapid evolution of Software-Defined Networking (SDN) has revolutionized modern communication systems by enabling dynamic, programmable, and intelligent network control. This study presents an AI-powered real-time communication framework integrated within SDN environments to enhance data transmission, risk analytics, and medical imaging workflows on the Oracle Cloud Infrastructure (OCI). Leveraging machine learning (ML) and deep learning (DL) models, the proposed system dynamically optimizes network routing, bandwidth allocation, and latency management for heterogeneous data streams. In the medical imaging domain, AI-driven analytics improve diagnostic precision and accelerate image processing by utilizing OCI’s scalable GPU-enabled resources. Furthermore, predictive risk analytics are employed to detect network anomalies, mitigate cyber threats, and ensure data integrity in compliance with healthcare data standards such as HIPAA. Experimental results demonstrate that the AI-optimized SDN framework achieves significant performance improvements in throughput, reliability, and decision latency compared to traditional SDN controllers. The proposed solution establishes a foundational step toward autonomous, intelligent, and secure communication ecosystems for high-stakes applications such as medical diagnostics and real-time risk management.References
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