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.
Blockchain technology apparently is a trivial innovation, but this technology has attracted huge investors in a very short period compared to other technologies, and it is still having a lot of potential applications. Smart contracts are making possible execution in an automated and safe way by using blockchain technology. Therefore, smart contracts are applied in this research for the expert system. This paper is about an expert system working with smart contracts and neural networks as the inference machine to decide on the sensors optimal distribution and taking actions when sensor readings are out of range: control lights, activating fire alarms, temperature alarms, etc. for all spaces (parks, schools, hospitals, etc.) in a smart city based on the needs, and likes of the expert system user. This expert system works using a blockchain structure on the EOSIO ecosystem with all data gathered by the sensors being saved in cloud online making internet of things environment and essential data saved in a blockchain node.
This paper proposes an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognition (ER). Based on Artificial Intelligence, this proposal uses audio-visual emotion patterns to determine psychiatric profiles to attend to the most urgent as a priority. BCNN architectures were used to identify emergency patterns. The results indicate that the proposed method is adequate for classifying and identifying audio-visual patterns using Deep Learning (DL) with Boltzmann’s restricted machines. It is concluded that it is sufficient to consider the audio-visible critical features from the patient’s face and voice for the proposed model to recognize a psychiatric services emergency for immediate action: the emergency with no control and the Emergency under control. User personal dynamic profiles are stored in the blockchain ecosystem since they are deemed sensitive data. System security is provided by blockchain and authentication uses non-fungible tokens (NFT) technology.
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