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Quantum communication based cyber security analysis using artificial intelligence with IoMT
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DOI：10.1007/s11082-023-06185-7.png)
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Connected electronic devices used in healthcare, such as small sensors and actuators and other cyber-physical devices, make up what is known as the Internet of Medical Things. By linking these devices, medical monitoring, analysis, and reporting may become more intelligent and autonomous, ultimately benefiting the patient. To solve these problems, a thorough plan for monitoring and assessing method health needs to be formulated. The application of traditional machine learning to massive datasets in a shared computing environment may lead to model training and computation that is either too slow to provide accurate results or too fast to produce erroneous results as a consequence of rushed training, both of which are detrimental. Key 6G enablers may be seen of as the upcoming paradigms of machine learning (ML), quantum computing, and quantum ML (QML), as well as their synergies with communication networks. This research presents a new machine learning approach to medical cyber physical system (CPS) predictive maintenance using swarm robots. In this case, we use fog computing-integrated soft sensors to conduct a thorough closed-loop dynamic analysis of the CPS predictive maintenance. Extreme learning utilising a weighted sliding window. The investigation of medical security is then conducted using a kernel decision making system based on probabilistic swarm robotics. In terms of training accuracy, energy use, convergence ratio, precision, and F_measure, an experimental study is conducted. The system's ability to (i) avoid unscheduled downtime and (ii) consequently increase operational availability sets it apart from other systems. The system aims to notify users as quickly as feasible of unusual operations and probable failure scenarios.
Keyword:
Cyber physical system
Swarm robot
Medical application
Machine learning
Convergence ratio
Quantum communication
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4
论文数:
10.0K
被引数:
1.8W
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