Utilizing Hybrid Cloud Computing with Machine Learning and Deep Learning to Enhance Privacy, Security, and Empower Patients

Abstract

This study investigates the effectiveness of Hybrid Cloud solutions in meeting the challenges encountered within the healthcare sector. Hybrid Cloud technology provides adaptable, on-demand services that empower hospitals and clinics to sidestep costly infrastructure upgrades and streamline maintenance costs. The scalability of cloud platforms addresses the fluctuating demands of the health and wellness industry, supported by fail-safes like disaster recovery and redundancy to ensure continuous service availability. At the heart of this infrastructure lies the Hybrid Health Cloud (HHC), serving as a central data repository for efficient information access and sharing. Nevertheless, obstacles emerge due to the time-consuming decryption and memory-intensive re-encryption processes inherent in HHC schemes. To counter these challenges, a novel approach integrates machine learning, deep learning, and Hybrid Cloud technologies, aiming to enhance system efficiency. Leveraging SHA-based algorithmic perspectives such as categorization, grouping, deep semantic networks, and quantum semantic networks, this study strives to improve both prediction accuracy and data protection.