Verma Prabal, Gupta Aditya, Kumar Mohit, Gill Sukhpal Singh
Department of Information Technology, National Institute of Technology, Srinagar, India.
Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India.
Internet Things (Amst). 2023 Oct;23:100828. doi: 10.1016/j.iot.2023.100828. Epub 2023 May 26.
Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.
医疗网络物理系统（MCPS）紧密集成了医疗对象网络。这些系统非常高效，已逐渐应用于医疗保健4.0中，以实现持续的高质量服务。医疗保健4.0涵盖众多新兴技术，其应用已在各种病毒爆发的监测中得以实现。作为一种不断发展的医疗保健趋势，利用MCPS可以治愈冠状病毒病（COVID-19）并防止其传播。这种病毒在人与人之间传播，可能会造成毁灭性后果。此外，随着全球死亡率和新病例惊人地上升，迫切需要持续需要持续识别和筛查感染患者以减轻其传播。基于这些事实，我们提出了一个利用新颖的工业5.0技术对COVID-19疫情进行早期检测、预防和控制的框架。所提出的框架在雾层中使用降维技术，使高质量数据可用于分类目的。雾层还使用基于集成学习的数据分类技术，根据症状数据集检测COVID-19患者。此外，在云层中，已进行社交网络分析（SNA）以控制COVID-19的传播。实验结果表明，与现有方法相比，所提出的框架在准确率（82.28%）、特异性（91.42%）、灵敏度（90%）和具有有效响应时间的稳定性方面取得了更好的结果。此外，在雾层利用基于CVI的警报生成提高了所提出系统的新颖性。
