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@article{192374,
        author = {Devika Rajesh Kolwadkar and Aakanksha Shukla and Dhanashree Torkade and Dikshant Dhawale and Divya Dehankar and Gaurav Chaudhari},
        title = {Intelligent Computational Approaches for Mental Health Assessment and Suicide Risk Mitigation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1641-1648},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192374},
        abstract = {Mental health problems and suicide are serious public health issues across the world. Detecting suicidal indicators at an early stage and delivering appropriate support can lower the likelihood of suicide. However, traditional mental healthcare systems often struggle because there are not enough mental health professionals and diagnoses are sometimes delayed. Artificial Intelligence (AI) can help solve these problems by analysing data, understanding human language, and interacting with users through chatbots. This paper studies how AI is used to support mental health and prevent suicide. It discusses AI methods such as suicide risk prediction, mental health chatbots, monitoring social media behaviour, and analysing emotional data from wearable devices. The paper also highlights important challenges, including ethical issues, data privacy concerns, and the need for responsible and safe use of AI in mental healthcare.},
        keywords = {Artificial Intelligence, Mental Health, Suicide Prevention, Machine Learning, Natural Language Processing, NLP, Chatbots, Predictive Analytics, Deep Learning, Mental Health Monitoring, Early Detection, Emotional Analysis, Social Media Analysis, Wearable Technology, Healthcare AI},
        month = {February},
        }