ABSTRACT
The corona virus has infected the entire world in the most severe ways. Many countries found the situation is very difficult to deal with and their health support infrastructure is not sufficient to manage the spread. People are locked in their homes and the whole world economy is in danger. That final vaccine has not yet reached the masses to deal with the epidemic. The corona virus, also known as COVID-19, can be spread by touching or coming close contact with an affected person, which is why the risk becomes so significant. However, the emergence of new emerging innovative technology such as blockchain and the Internet of Things (IoT) has changed the healthcare sector, especially in preventive measures. Different devices have ushered in a new era in the field of symptom-based diagnosis where doctors can most easily identify a person with a corona infection. This article presents a robust health-based IoT systems that can strengthen full COVID-19 administration and achieve greater results with available resources. The simulation results confirm the effectiveness of the proposed infection detection system.
ABSTRACT
The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti-corona virus-Henry gas solubility optimization-based deep maxout network (ACV-HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV-HGSO is designed by incorporating anti-corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi-objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension-reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively. © 2022 John Wiley & Sons, Ltd.