Department of Pharmaceutics, College of Pharmacy, Qassim University Buraydah, Buraydah, Saudi Arabia.
Prince Faisal Bin Mishaal Artificial Intelligence Chair, Qassim University, Buraydah, Saudi Arabia.
Comput Intell Neurosci. 2022 Sep 29;2022:4174805. doi: 10.1155/2022/4174805. eCollection 2022.
Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability.
现在，具有快速响应率的雾辅助和物联网启用的智能医疗保健系统是主要关注点。动态和异构的雾网络难以管理，并且在管理不断增加的雾节点上的负载时可能会意识到相当大的开销。雾计算在管理来自各种基于物联网的应用的不断增加的处理需求方面起着至关重要的作用。智能医疗保健系统在基于传感器的设备的协助下工作，并且自动数据收集和处理可以加快整个系统的功能。在提出的工作中，提出了一种新颖的智能医疗保健框架，其中执行了一系列活动，主要目标是减少延迟和执行时间。主成分分析用于特征减少，并且支持向量机具有径向基函数核用于分类目的。使用遗传算法在雾节点上实现工作负载优化。数据收集过程还涉及预处理，作为生成更清洁数据的领先步骤。智能和优化技术在提出的框架中的融合确实提高了整个系统的效率。实验结果表明，提出的工作在延迟、执行时间、整体系统准确性和系统稳定性方面优于现有的雾辅助智能医疗保健系统。
