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Cyber Security in Healthcare with Artificial Intelligence

The intelligent Internet of Things (IoT), -which provides an endless number of networking options for the examination of medical data, is improving the connection between technology and healthcare society. Deep networks have seen productive alterations in recent years, and the usage of health wearables has become more prevalent The Internet of Things, which was made possible by deep neural networks, resulted in innovative social breakthroughs in medicine and opened up new avenues for the investigation of healthcare data. Despite the progress that has been made, there are still a few issues that need to be resolved in terms of the quality of the service. The Grey Filter Bayesian Convolution Neural Network, or GFB- CNN, is a method of smart healthcare that is powered by Deep Neural Networks and takes the use of real-time data This technique is presented here as part of this study. In this paper, we proposed an all-encompassing Al-driven Internet of Things (IoT) eHealth architecture that makes use of an ICNN in order to increase accuracy and efficiency across the essential quality of service parameters. An extensive Mobile HEALTH (MHEALTH) dataset is analyzed in order to determine how effective the approach is in its intended use. This illustrative example investigates and covers all of the pertinent aspects of the recommended technique, from design concepts to matching accuracy, overhead costs, and the amount of time associated with state-of-the-art procedures. The ICNN strategy was evaluated in a variety of simulated environments alongside other approaches that are considered to be state of the art We show that our method effectively analyses health information investigations for cardiac indications by being able to differentiate between healthy and abnormal heart signals in an efficient manner at a cheap cost and in a short amount of time, both of which are necessary for sensing and data collection.