Exploring The Synergy Between Emotional Intelligence and Artificial Intelligence: Enhancing Human-Machine Interaction and Decision-Making for Employees Mental Health

Emotion detection in image processing is a burgeoning field with applications in facial recognition and human-computer interaction. This provides a brief insight into the significance and methods of emotion detection from images. Techniques such as deep learning and facial landmark analysis are employed, offering promising avenues for improved technology-human interaction. Challenges include handling diverse expressions and ensuring ethical data use. Efficient Net-BiGRU, a novel neuronal system construction, leverages Bidirectional Gated Recurrent Units (BiGRU) to competently excerpt rich structures from input data. This design enhances feature representation in a computationally efficient manner, making it adaptable for diverse applications. The highlights integration of EfficientNet and BiGRU for effective feature extraction. The Hunger Games Search Optimization (HGSO) algorithm, in conjunction with Attention Gated Recurrent Unit (AttGRU) model, presents an innovative approach for emotional prediction. This mixture model combines the powerful search capabilities of HGSO with the attention mechanism in AttGRU to accurately capture and interpret emotional cues in data. The abstract underscores the synergy between optimization and deep learning for emotion prediction tasks, offering promising results in various applications. This tool provides a user-friendly and efficient means of analysing and processing the employee dataset, facilitating research and experimentation in the field of employee engagement tracking.