Depression Calculator as a Final Project for Datamining Subject in the College

Prompt: والحرب تجعل كل شيءٍ واضحٍ .... 🖊 Output: No depression e` Propability of 99.9% 🧠⏳

Analyzed time-series data (Depressjon) to detect depression from patient activity recorded via clinical actigraphy watches. Utilized features such as time domain, statistical metrics, and LSTM-extracted attributes.

se diseñó un modelo para detectar depresión en usuarios en base a comentarios en redes sociales

This Project creates a smart AI computer program that predicts depression from what people write online. It learns patterns in language to spot signs of depression, helping identify those who might need support.

Text-Based Depression Detection By Machine Learning

T.E.D.D.Y aims to help teachers and counselors analyze student essays to find potential depressive/suicidal sentiment and offer support.

Depression Detect is a web app which enables clinicians to use sensing technologies with a focus on acoustic characteristics and facial landmarks to detect depression.

Harnessing large language models over transformer models for detecting Bengali depressive social media text: A comprehensive study

This is a depression detection system that detects depression in Sinhala-English code-mixed text content which are published by different users on social media. The frontend of the system was developed using Bootstrap, HTML, and Jquery and the backend of the system was developed using Flask

Analysis of labeling strategies aimed at identifying depression phenomena among users’ tweets.

no description, just suffering

This is a repo for a side project about detecting depression using ML

Depression detection with federated learning

Depression Asisstant can help you give solution. Please using Python version 3.9.5 for contribute.

This is an academic final year individual project, published by Liew Jun Yen - Data Analyst

Undergraduate Project - Application of Monitoring and Recording Depression Emotions

Internal Healthn uses AI technology and social media as a tool for mental health care