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@article{181856,
        author = {Dr.V.P.kKrishnammal and Dr.N. Bagyalakshmi and S. Sivaranjani},
        title = {INTERNALIZING - DISORDER CHATBOT FOR BIOMEDICAL APPLICATIONS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {4421-4425},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181856},
        abstract = {Automatized mental health chatbots are conversationally built with technology in mind with having the potential to reduce efforts to healthcare costs and improve access to medical services and knowledge. Internalizing Disorder chatbot introduces an innovative mental health assistance chatbot system designed to address queries related to depression, anxiety, and suicidal thoughts. Leveraging a Sequence-to-Sequence (Seq2Seq) neural network architecture implemented with PyTorch, the chatbot enhances its natural language understanding and response generation capabilities. The model comprises of an encoder, a decoder and two Recurrent Neural Networks (RNNs). The encoder processes user input, such as medical queries, into a fixed-size context vector. The decoder then generates a sequence of words representing the chatbot's response. By training on a diverse dataset encompassing various mental health intents, the model learns to provide contextually relevant and empathetic responses. The graphical user interface developed using PyQt5 enables individuals to express their concerns and receive supportive and informative responses from the Chatbot. Integration of Pyttsx3 voice library for conveying chatbot responses audibly, enhancing user engagement and accessibility. Internalizing Disorder chatbot showcases the potential of Seq2Seq models in addressing internalizing disorder challenges, offering a compassionate and accessible resource for those in need of support and guidance.},
        keywords = {Recurrent Neural Networks, PyQt5, Seq2Seq models, chatbot, Natural language processing.},
        month = {October},
        }