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@article{201126,
        author = {Shubham Kamble and Shlok Ekhande and Roshan Jaybhaye and Sagar Ingole and Prof. A. A. Bamanikar},
        title = {AI-Powered Mental Health Monitoring And Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2868-2875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201126},
        abstract = {The AI-Powered Mental Health Monitoring and Recommendation System is a webbased application developed to help users track and improve their emotional well-being in a simple and effective way. In today’s busy lifestyle, people often ignore their mental health, which can lead to stress, anxiety, and other emotional issues. This system provides a smart solution by using Artificial Intelligence to monitor user emotions on a daily basis. The application collects user input through daily questions, which are dynamically generated using the Gemini API. These questions help in understanding the user’s thoughts, feelings, and daily experiences. The responses provided by the user are then processed and analyzed using a Machine Learning model trained for emotion classification. Based on the input, the model predicts the user’s current emotional state, such as happy, sad, stressed, or relaxed. After predicting the emotion, the system provides personalized video recommendations to help improve or maintain the user’s mood. For example, if the user is feeling sad, the system suggests motivational videos, while for stress, it recommends relaxing or meditation content. This makes the system interactive and supportive for users. All user responses and predicted emotions are stored in a database, allowing users to view their past records and track their emotional changes over time. The system also provides graphical representations of mood trends, helping users understand their emotional patterns and identify possible causes of stress or happiness. The entire system is developed using Flask for backend processing, along with Machine Learning techniques for prediction and API integration for dynamic interaction. This project combines Artificial Intelligence, web development, and data visualization to create a practical and user-friendly mental health support system. Overall, the system not only predicts emotions but also encourages self-awareness and promotes better mental health management through continuous monitoring and personalized recommendations.},
        keywords = {Artificial Intelligence, Machine Learning, Mental Health Monitoring, Emotion Detection, Mood Prediction, Recommendation System, Flask Web Application, Gemini API, Text Classification, Data Visualization.},
        month = {May},
        }