Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare

Abstract

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.