Design of an Automated Recurrent Neural Network for Emotional Intelligence Using Deep Neural Networks

Emotional intelligence (EI) is a collection of quasi skills, attitudes, and talents that influence one's ability to respond quickly to environmental changes and stresses. Nevertheless, it is not always possible to monitor the effect of a multitude of variables involved in behavioral phenomena. In several different industries, Emotion Perception or Artificial Emotional Intelligence is now a $20 billion research area with applications. In a variety of ways, artificial emotional intelligence can operate through industries. Emotional readings may also be used by AI as relating to decision, such as in advertising campaigns. In terms of temporal dynamics, a powerful learning method is required to extract high-level representations of emotional responses. In terms of spatial dispersion, a learning process strategy is required to extract high-level assessment of emotional states. The recurrent neural network outperforms linear models in terms of prediction accuracy. A recurrent model is proposed in this paper to forecast the pattern between the variables of age, gender, occupation, marital status, and education in order to predict the EI. The appropriate recurrent model is capable of predicting EI with important correlations in most of its dimensions and could demonstrate the advantage over regression models in predicting EI using sociological parameters. This model will estimate the level of EI in the various occupational, professional, gender and age groups and provide a planning basis for addressing possible deficiencies in each group.