Emotional intelligence for cognitive internet of things based smart environments

Abstract

In today s extravagant era, the capacity to perceive feeling is one of the signs of passionate newlineinsight, a part of human knowledge that has been contended to be significantly more imperative newlinethan scientific and verbal intelligences. Due to gradual enrichment in IoT technology for smart newlineenvironment level, Technology disruptions and degradation of performance in the industries, newlineworkers have lost their interest or concentration in work activity and have also lost their focus or newlineperformance in the working environment. In addition, despite the rapid growth of IoT, In the newlinefield of modern intelligent service, the current IoT based systems significantly lacks cognitive newlineintelligence this implies cannot fulfill the requirements for industrial services. newlineDeep learning is become one of the most popular technique that takes place in many machine newlinelearning related applications and studies. While it is put in the practice mostly on content based newlineimage retrieval, there is still room for improvement by employing it in diverse computer vision newlineapplications. As per the rigorous theoretical and practical analysis, it has been found that an newlineimmediate need to address this issue by developing an emotional intelligent approach, Machine newlinelearning (deep learning, CIoT), which will mentor and counsel workers by monitoring their newlinebehavior in the work environments. newlineIn this study, we aimed to construct a CNN model based emotional intelligence System (EIS), in newlineorder to automatically classify expressions presented in Facial Expression Recognition newline(FER2013) and kaggel image database. Our presented model achieved % 81.1, success rate on newlineFER2013 database. newlineKeywords: Deep Learning, Emotional Intelligence, Facial Expression Recognition, Image newlineClassification Prediction, Convolution Neural Networks. newline