11th International Conference on ICT for Sustainable Development

Authors - Ankita Mehta, Shailesh Gahane
Abstract - This paper discusses about, we display a profound learning-based approach bipolar clutter discovery utilizing Convolutional- Neural Systems (CNN) and Long Short-Term Memory (LSTM) systems. To assess the model’s performance, two particular datasets Twitter information and survey data were analyzed. Preprocessing steps, counting information enlargement, normalization, and the application of the Adam optimizer, were joined to upgrade the model’s adequacy. The model’s exactness and misfortune were measured for both datasets, and it was watched that the survey dataset given superior execution, yielding higher precision and lower misfortune compared to the Twitter dataset. These discoveries propose that the questionnaire-based information may be more reasonable for solid bipolar disorder location within the given show. The inquire about illustrates the potential of combining CNN and LSTM for mental wellbeing examination, highlighting the significance of information determination in accomplishing ideal comes about.