Enhancing the Accuracy of Health Care Internet of Medical Things in Real Time using CNNets

Abstract

This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

“…However, this theoretical equilibrium between the two networks only exists in theory, and actual GAN training has its own set of issues. The first is the instability of GAN training, and the second is the network collapse, which causes the generation process to collapse [3] and [4].…”