Mirza Olfat M, Mujlid Hana, Manoharan Hariprasath, Selvarajan Shitharth, Srivastava Gautam, Khan Muhammad Attique
Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah 24381, Saudi Arabia.
Department of Computer Engineering, Faculty of Computer Engineering, Taif University, Taif 21974, Saudi Arabia.
Diagnostics (Basel). 2022 Nov 10;12(11):2750. doi: 10.3390/diagnostics12112750.
To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods.
为避免出现严峻情况，医疗部门必须开发各种方法，以便在偏远地区快速准确地识别感染情况。拟开展工作的主要目标是创建一种利用物联网（IoT）执行多项监测任务的可穿戴设备。为减少通信损失量以及检测前所需的等待时间，并提高检测质量，所设计的可穿戴设备还采用了多目标框架进行操作。此外，还建立了一种可穿戴物联网设备的设计方法，利用不同的数学方法来实现这些目标。结果，监测到的参数值被保存在一个不同的物联网应用平台中。由于拟开展的研究聚焦于多目标框架，因此将状态设计与深度学习（DL）优化技术相结合，降低了可穿戴技术中检测的复杂性。具有物联网流程的可穿戴设备甚至已被纳入当前方法中。然而，无法使用数学方法和优化策略来复制解决方案。因此，所开发的可穿戴设备可应用于实时医疗应用，以便对个人进行快速远程监测。此外，对所提出的技术进行了实时测试，并利用一个物联网仿真工具来跟踪五种不同情况下的对比实验结果。在所有审查的案例研究中，所规划的方法比当前的最先进方法表现更好。
