Smart Healthcare Based Cyber Physical System Modeling by Block Chain with Cloud 6G Network and Machine Learning Techniques

Smart healthcare cyberphysical systems are systems of future because they combine physical as well as digital realms to enable the effective operation of medical procedures and patient care through external patient monitoring, patient control, and equipment monitoring and exchange of physiological data. Digital generation, electronic storage, and remote access by medical staff or patients are the processes involved in the communication, device, information system interaction of the smart healthcare cyber physical system (CPS). Intelligent network orchestration and administration are key components of the 6G future of networks, even though the cloudification of micro-services-oriented networks is a well-known element of 5G. As a result, the envisaged 6G paradigm heavily relies on artificial intelligence (AI), machine learning (ML), deep learning (DL). Foundation of this suggested strategy is a patient-centric design that protects data on a reliable device, such as end users' smartphones, gives them discretion over data sharing access.This study uses ensemble deep learning architectures to suggest new methods for identifying malevolent users accessing healthcare data in EHRs. Here, monitoring data for the healthcare data access control system has been collected and processed for analysis of hostile activity in the networks utilising a Cognitive Blockchain monitoring control system with an Internet of things (IoT) module for data transfer. After the monitored data was initially processed, this will additionally look for any missing or altered data from individuals who are not identified. Next, employing ensemble classification with a fuzzy network combined with a kernelized reinforcement learning model, derived from the examination of the observed data. Here experimental analysis is carried out in terms of monitoring as well as classification systems. Parametric analysis of monitoring system is network security of 98%, privacy analysis of 93%, network monitoring analysis of 90%, delay of 55 ms, throughput of 95% and classification parameters are accuracy of 96%; precision obtained by proposed technique is 91%; recall obtained by proposed technique in 82%; F-score of proposed technique is 80%; proposed technique obtained MAP of 69%, RMSE obtained by proposed technique is 65%, computational cost of proposed technique is 49%.As per journal instructions, Author biography and photo is mandatory. Kindly provideyes we have provided