Blockchain cnn deep learning expert system for healthcare emergency

Abstract

This paper relates to the field of Artificial Intelligence, specifically to image recognition, and provides an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognitions (ERs) using audio-visual emotion patterns to determine a healthcare emergency to be attended. BCNN architectures were used to identify emergency patterns. The results obtained indicate that the proposed method is adequate for the classification and identification of audio-visual patterns using deep learning (DL) with Restricted Boltzmann Machines (RBMs). It is concluded that it is sufficient to consider the audio-visible key features obtained from the patient's face and voice of the proposed model to recognize a healthcare emergency for immediate action. "Sense of urgency"and "with urgency but with self-control"are the emotion profiles considered for a healthcare emergency, and user personal emotion profiles are stored in the Blockchain ecosystem since they are deemed sensitive data.