Personalized Emotion Recognition and Emotion Prediction System Based on Cloud Computing

Abstract

Promoting economic development and improving people’s quality of life have a lot to do with the continuous improvement of cloud computing technology and the rapid expansion of applications. Emotions play an important role in all aspects of human life. It is difficult to avoid the influence of inner emotions in people’s behavior and deduction. This article mainly studies the personalized emotion recognition and emotion prediction system based on cloud computing. This paper proposes a method of intelligently identifying users’ emotional states through the use of cloud computing. First, an emotional induction experiment is designed to induce the testers’ positive, neutral, and negative three basic emotional states and collect cloud data and EEG under different emotional states. Then, the cloud data is processed and analyzed to extract emotional features. After that, this paper constructs a facial emotion prediction system based on cloud computing data model, which consists of face detection and facial emotion recognition. The system uses the SVM algorithm for face detection, uses the temporal feature algorithm for facial emotion analysis, and finally uses the classification method of machine learning to classify emotions, so as to realize the purpose of identifying the user’s emotional state through cloud computing technology. Experimental data shows that the EEG signal emotion recognition method based on time domain features performs best has better generalization ability and is improved by 6.3% on the basis of traditional methods. The experimental results show that the personalized emotion recognition method based on cloud computing is more effective than traditional methods.

Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions

“…The EEG sequences H r and U r are input into the first BGRU layer of the spatial and temporal encoders composed of 2BGRUA. The process up to the second layer of BGRU is performed using Equations ( 3)- (6). The PSD feature expression 𝑺 𝒓 , obtained through preprocessing and spectrum analysis, is configured as in Equations ( 1) and ( 2) and input into spatial and temporal encoders in parallel.…”

“…The EEG sequences 𝐇 𝒓 and 𝐔 𝐫 are input into the first BGRU layer of the spatial and temporal encoders composed of 2BGRUA. The process up to the second layer of BGRU is performed using Equations ( 3)- (6).…”

“…Emotions are crucial in daily life and significantly influence behavior, communication, thinking, and mental health [ 5 ]. Thus, if machines can recognize human emotions, they offer innovative potential in various applications, including designing more effective computer systems, providing personalized services, and diagnosing and treating mental health [ 6 ].…”

Retracted: Personalized Emotion Recognition and Emotion Prediction System Based on Cloud Computing

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”