Suicide Detection from different languages using Python

This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.

A pre-trained transfer model based on distilbert-base-uncased for detecting textual suicidal thoughts, utilizing Python

A Python project to classify suicidal text messages using machine learning with 93.33% accuracy.

A comparison of three discriminative machine learning models (Logistic Regression, Long Short-term Memory, Transfoermer) to determine which is best suited for suicide ideation detection

Suicide Ideation Detection in Indonesian Language Twitter Posts Using IndoBERT and LSTM/CNN

AI-powered suicide risk classification system with explainable predictions using SHAP and LIME.

A RESTful API that embeds classification with confidence of textual suicidal thoughts, conducted on a previously compiled transformer model, utilizing Python

A web application that classifies an entered text into either suicidal or non-suicidal, utilizing Flutter

A deep learning model for detecting suicidal tendencies in text using LSTM and GloVe embeddings. Includes interactive text input with IPyWidgets for real-time analysis.

A research-focused project aimed at early detection of suicide risk by analyzing both textual and visual data. The system integrates a fine-tuned DistilBERT model combined with CNN for understanding linguistic patterns in user input, and uses YOLO for analyzing facial expressions, gestures, and behavioral cues from images or videos.

🛡️ Classify text messages for suicide risk with a FastAPI service, enabling timely interventions using a 93.33% accurate machine learning model.