Real-Time Cloud-AI Framework for Risk Prediction and Intelligent Network Management using Deep Learning
DOI:
https://doi.org/10.15662/IJARCST.2025.0806802Keywords:
Artificial Intelligence, Cloud Computing, Deep Learning, Real-Time Analytics, Risk Prediction, Network Management, Anomaly Detection, Intelligent SystemsAbstract
The increasing complexity of modern network infrastructures demands intelligent, adaptive, and real-time solutions for proactive risk management. This paper proposes a Real-Time Cloud-AI Framework that integrates deep learning techniques for predictive risk assessment and intelligent network management. The framework leverages cloud computing to ensure scalability, fault tolerance, and seamless data integration across distributed systems. Artificial intelligence models, particularly deep neural networks, are employed to detect anomalies, predict potential failures, and optimize network performance in real time. By continuously analyzing large-scale network data streams, the system provides early warnings and automated responses to minimize downtime and security threats. Experimental validation demonstrates improved accuracy, response time, and reliability in managing complex network environments. This hybrid cloud-AI architecture offers a robust foundation for intelligent, self-learning, and secure digital ecosystems.
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