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Intelligent Healthcare Provisioning in Fog Using Grey Wolf Optimization
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Author(s): Rajalakshmi Shenbaga Moorthy (Sri Ramachandra Institute of Higher Education and Research, India), K. S. Arikumar (VIT-AP University, India), Sahaya Beni Prathiba (Vellore Institute of Technology, Chennai, India)and P. Pabitha (Anna University, India)
Copyright: 2024
Pages: 20
Source title:
Computational Intelligence for Green Cloud Computing and Digital Waste Management
Source Author(s)/Editor(s): K. Dinesh Kumar (Amrita Vishwa Vidyapeetham, India), Vijayakumar Varadarajan (The University of New South Wales, Australia), Nidal Nasser (College of Engineering, Alfaisal University, Saudi Arabia)and Ravi Kumar Poluru (Institute of Aeronautical Engineering, India)
DOI: 10.4018/979-8-3693-1552-1.ch016
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Abstract
The increasing population rate plays a vital role in bringing challenges in the provisioning of health care. The data was initially collected and kept in the cloud, where the machine learning algorithm was run on the data and decisions were then transmitted back to the client device. This incurs a significant delay for transferring the data and getting back the result. Thus, in this chapter, fog layer is introduced between device layer and cloud layer for processing the sensor data. The introduction of fog layer tends to minimize the delay incurred by the cloud, as analyzing the health data is close to the device that generates the data. For conducting the best analytics on health data received from sensors, the grey wolf optimization (GWO)-based k-nearest neighbor (K-NN) is proposed. GWO K-NN is integrated in the fog nodes, which is close to the device generating the health data, thereby providing timely decisions. The proposed GWO K-NN works on the fitness of accuracy and misclassification rate of K-NN, and it models the hunting behavior of wolves.
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