Privacy-Preserving Zero-Touch AI Architecture for Predictive Healthcare Logistics for DC-DC Converter: Integrating NLP with Cloud and Cyber Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2024.0705004Keywords:
AI-driven healthcare, zero-touch automation, privacy-preserving architecture, natural language processing, cloud intelligence, cyber intelligence, predictive logistics, DC-DC converter optimization, federated learning, intelligent energy systems, sustainable healthcare, secure data exchangeAbstract
This paper introduces a privacy-preserving zero-touch AI architecture that integrates Natural Language Processing (NLP) with cloud and cyber intelligence to enhance predictive healthcare logistics and DC-DC converter optimization. The proposed framework enables intelligent automation for healthcare operations by leveraging NLP-driven contextual analysis, cloud-based data orchestration, and cyber-resilient decision mechanisms. Through real-time data interpretation and federated learning, the system ensures secure collaboration among distributed healthcare nodes while maintaining patient data confidentiality. The integration of DC-DC converter analytics supports intelligent energy regulation, improving power efficiency and system reliability in medical infrastructure. The architecture emphasizes zero-touch operations, minimizing human intervention, optimizing logistics workflows, and strengthening cyber defense in healthcare systems. This multidisciplinary approach contributes to sustainable, intelligent, and secure healthcare ecosystems through the convergence of AI, energy systems, and cloud-based intelligence.
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