Implementing Human Emotion Detection using Convolutional Neural Networks: An Optimized CNN-RFE-Attention Approach

The application of Convolutional Neural Networks (CNNs) for emotion detection has become a crucial field within artificial intelligence which allows healthcare organizations along with human-computer interaction developers to conduct sentiment analysis. The paper investigates emotion recognition from facial expressions and speech through CNN architectures to achieve better accuracy results. Different CNN models integrate LSTM networks with recursive feature elimination techniques to achieve outstanding results in feature extraction and classification operations. Two key domains where real-time emotion recognition systems become increasingly common include educational institutions as well as customer support operations and systems meant to monitor mental health indicators. This research performs a detailed examination of latest research developments before it evaluates different model results while analyzing specific issues involving dataset bias as well as system efficiency for real-world implementations. Evaluation of modern CNN structures will generate information to improve emotional detection accuracy and operational speed for future developments in deep learning affective computing systems.