Health Monitoring in IoT with Context-Aware Deep Convolutional Spiking Neural Network and Woodpecker Mating Algorithm

Abstract

ABSTRACT In recent years, many context-sensitive approaches have been established to offer physiological information about the wellness of each person. While the patient’s health is being monitored, there are delays in the cloud data transfer. To overcome these delays, a Deep Convolutional Spike Neural Network (DCSNN) with a Woodpecker Mating Algorithm (WMA) is proposed in this manuscript for monitoring the healthcare data in the Internet of Things (IoT)-based context-aware architecture (DCSNN-WMA-HCM-IoT-CAA). Here, the input data are amassed from real-time datasets. Then the data are supplied to the pre-processing. The pre-processing data involve the assortment phase, data storage phase, and data redundancy phase. In the redundancy phase, the Kernel co-relation method is employed to remove the repeated data. The pre-processing output is given to the feature extraction. The feature is extracted under the fast discrete Curvelet transform method. After that, the extracted features are given to DCSNN optimized with WMA to classify regular, irregular, and critical conditions of the patient. The proposed DCSNN-WMA-HCM-IoT-CAA method is activated in OMNeT++. The proposed DCSNN-WMA-HCM-IoT-CAA approach attains 4.08%, 8.17%, 9.32%, and 5.17% high accuracy, 14.3%, 15.4%, 19.51%, and 27.81% lower computation Time and 12.29%, 15.36%, 11.55%, and 13.91% higher AUC compared with existing methods.