Intelligent System Design Using Machine Learning for Emotion Recognition and Rectification

Abstract

Emotion recognition and rectification is a critical area of research in the field of artificial intelligence. With the increasing availability of data and advances in machine learning algorithms, this field has become increasingly important. In this project, we propose a machine learning-based intelligent system design for emotion recognition and rectification. The proposed system utilizes a combination of supervised and unsupervised machine learning algorithms to automatically recognize and rectify emotional states. The system is designed to analyze data from both video and audio sources to identify different emotions and their associated characteristics. The system also uses natural language processing to identify and understand the context in which the emotion is expressed. Once the emotion is identified, the system then recommends appropriate corrective actions to help the user manage the emotion. Additionally, the system provides feedback on the effectiveness of the rectification actions taken. This project could potentially benefit individuals with mental health issues, as well as organizations that need to monitor and manage the emotions of their employees.