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AI-Driven Cloud Infrastructure Powered by Machine Learning for Healthcare Governance and EV Platforms

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the digital landscape of both healthcare governance and electric vehicle (EV) platforms. This paper proposes an AI-driven cloud infrastructure model that integrates ML-powered analytics, automated decision support, and scalable cloud computing to enhance governance, security, and operational efficiency in healthcare systems and EV platforms. The framework focuses on data interoperability, real-time analytics, predictive modeling, and adaptive resource allocation to optimize patient outcomes, regulatory compliance, and energy management. For healthcare governance, the system supports predictive disease surveillance, fraud detection, resource optimization, and policy enforcement through secure data sharing and robust privacy controls. For EV platforms, the infrastructure enables intelligent charging management, demand forecasting, route optimization, and vehicle-to-grid (V2G) coordination. The model uses a multi-layer architecture comprising data ingestion, processing, analytics, and governance layers, leveraging cloud-native services and ML algorithms such as deep learning, reinforcement learning, and anomaly detection. The research adopts a mixed-methods approach including system simulation, performance benchmarking, and qualitative stakeholder analysis to validate effectiveness. Findings indicate improved decision-making speed, enhanced data security, reduced operational costs, and increased system adaptability, highlighting the potential of AI-driven cloud infrastructure in critical sectors

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