Cloud Computing Intelligence based Framework for Adaptive Nursing Education with Artificial Intelligence Monitoring and Blockchain Certification

Abstract

This research presents an intelligent cloud-based framework for online nursing education systems, leveraging AI-driven resource allocation to optimize computational efficiency while ensuring compliance with healthcare training standards. The proposed system dynamically manages cloud resources to support data-intensive applications like virtual simulations and AI proctoring, achieving 92% resource utilization with 35% cost reduction compared to traditional approaches. Through a hybrid machine learning model combining predictive analytics and real-time monitoring, the solution demonstrates 40% faster response times for clinical simulations and maintains 99.9% availability during peak usage periods. The architecture incorporates specialized security measures for sensitive patient data and blockchain-based credential verification, addressing critical challenges in nursing education technology. Performance evaluations against conventional methods show superior scalability, with the system supporting 50,000+ concurrent users while reducing energy consumption by 22%. Results indicate significant improvements in key metrics: throughput (2,000 tasks/sec), latency (<200ms for VR labs), and fault tolerance (99.9% success rate). The framework's adaptive capabilities ensure optimal performance across diverse nursing education workloads, from routine coursework to high-stakes assessments. This study establishes a practical, scalable model for cloud-based nursing education that balances pedagogical requirements with technical constraints, offering institutions a cost-effective pathway to digital transformation while maintaining rigorous academic and data security standards.

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