An assessment model for emotion advisor for autistic children using deep learning

A major obstacle that impacts the social and emotional well-being of people with mental instability is cognitive impairment, especially the difficulty of having a limited capacity for deep thought. In this disorder, people have trouble communicating and interacting with others due to neurological issues. Helping children with mental disorders learn social norms has been a major focus of assistive technology in recent years. The ”Emotional Advisor,” a new tool presented in this study, makes use of state-of-the-art deep learning methods like Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN). For children who are very reserved, the emotional advisor can be a lifesaver when it comes to having meaningful conversations and developing their emotional intelligence. According to the experiments’ findings, the proposed model using CNN with Bi-LSTM is more effective than conventional machine learning techniques, with an accuracy of 95% that outperforms the conventional models.