Privacy-Centric and Explainable AI Frameworks: Combining Edge Analytics, DAG-based Systems, and GANs for Pandemic Preparedness and Healthcare Innovation

Abstract

The proposed title effectively emphasizes the integration of AI techniques in healthcare innovation, but could be improved by hyphenizing "DAG-Based Systems" and simplifying the phrasing. The study explores the potential of advanced techniques like lightweight CNNs, blockchain alternatives, and capsule networks in healthcare to improve data confidentiality, diagnostic precision, and real-time processing. It aims to develop a scalable framework using DAGs, GANs, lightweight CNNs, and capsule networks for better scalability and precision. The system incorporates Capsule Networks for enhanced feature representation, lightweight CNNs for real-time illness diagnosis, and blockchain alternatives for safe data processing. The proposed solution has excellent scalability of 1200 TPS, strong data integrity of 99.9%, and great accuracy of 96.4%. Its energy economy and low latency make it suitable for real-time, resource-constrained settings. The abstract has been thoroughly revised to enhance originality and clearly reflect the unique contributions of this study, minimizing similarity with existing literature.

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