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.

Artificial intelligence‐based blockchain solutions for intelligent healthcare: A comprehensive review on privacy preserving techniques

“…With the involvement of Artificial Intelligence specifically Image recognition provides an innovative approach to Blockchain Convolution Neural Networks to ascertain a healthcare emergency 152 . Discussed Blockchain Convolution Neural Networks and audio‐visual patterns using Deep Learning with Restricted Boltzmann Machines (RBM).…”

“…The emergency cases taken during model implementation have a higher accuracy rate but for the non‐healthcare emergency profile accuracy rate is not promising. The whole architecture 152 was performed with only 13 basic profiles of emotions, if we increase the emotions to hundreds the efficiency would be reduced 152 . Discussed that better accuracy can be achieved if we use more powerful hardware.…”

“…With the involvement of Artificial Intelligence specifically Image recognition provides an innovative approach to Blockchain Convolution Neural Networks to ascertain a healthcare emergency. 152 Discussed Blockchain Convolution Neural Networks and audio-visual patterns using Deep Learning with Restricted Boltzmann Machines (RBM). The emergency cases taken during model implementation have a higher accuracy rate but for the non-healthcare emergency profile accuracy rate is not promising.…”

“…The whole architecture 152 was performed with only 13 basic profiles of emotions, if we increase the emotions to hundreds the efficiency would be reduced. 152 Discussed that better accuracy can be achieved if we use more powerful hardware.…”

Blockchain and machine learning in the internet of things: a review of smart healthcare

“…On the other hand, Chen et al [25] presented a healthcare system for diabetes disease detection using a variety of ML classification methods supported by blockchain and the innovative agent model to process real-time medical data. Moreover, the proposed expert system is based on a combination of deep learning and blockchain of convolutional neural networks (BCNN), for healthcare emergencies [26]. The authors used BCNNs to provide a novel way for emotion recognition (ER) and for use in healthcare emergencies.…”