A Deep Learning Algorithm for Mental Health Support using Artificial Intelligence (AI) Robot with Machine Learning - Santos, Daisy-Ann Ylarde; Sultana, Maksuda (International Journal of Computing Sciences Research; Vol 8 (2024): Volume 8, 2024)
Purpose – This study explores the feasibility and effectiveness of utilizing a deep learning algorithm integrated into an AI robot to provide mental health support. Method – The research employs deep learning techniques and machine learning algorithms to develop an AI-powered robot capable of understanding and responding to human emotions and mental health needs. The algorithm is trained on a diverse dataset of mental health-related information, including text, audio, and visual inputs, to enhance its comprehension and response capabilities. Results – Initial testing of the AI robot demonstrates promising results in its ability to accurately recognize and respond to various emotional cues and mental health states exhibited by users. The deep learning algorithm enables the robot to adapt and personalize its interactions based on individual preferences and needs, enhancing its effectiveness as a mental health support tool. Conclusion – Integrating deep learning algorithms into AI robots holds significant potential for revolutionizing mental health support services. By leveraging advanced technologies, such as natural language processing and computer vision, these robots can provide personalized and accessible assistance to individuals experiencing mental health challenges. Recommendations – Future research should focus on expanding the dataset used for training the deep learning algorithm to encompass a broader range of cultural and demographic backgrounds. Additionally, efforts should be made to enhance the interpretability and transparency of the AI system to foster trust and acceptance among users and healthcare professionals. Practical Implications – The development of AI-powered robots for mental health support has practical implications for healthcare providers, policymakers, and individuals seeking assistance. These technologies have the potential to supplement existing mental health services and improve access to care, by seeking help for mental health concerns.