depression-detection
There are 17 repositories under depression-detection topic.
sidmulajkar / sentiment-predictor-for-stress-detection
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
Language:Jupyter Notebook 79hariharitha21 / Detection-of-Anxiety-and-Depression
Detecting Anxiety and Depression using facial emotion recognition and speech emotion recognition. Written in pythonPython
Language:Jupyter Notebook 47cosmaadrian / time-enriched-multimodal-depression-detection
Official source code for the paper: "It’s Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers"
Language:Python 37nitrotap / mental-health-check
A mental health quiz app to help individuals check in with themselves.
Language:JavaScript 27eddieir / Depression_detection_using_Twitter_post
depression detection by using tweets
Language:Python 23Akshat2430 / Detecting-Depression-From-Speech-Signals
Speech-based diagnosis of depression
Language:Python 21rafalposwiata / depression-detection-lt-edi-2022
This repository contains the code of our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022.
Language:Python 21- Language:Jupyter Notebook 21
Official source code for the paper: "Reading Between the Frames Multi-Modal Non-Verbal Depression Detection in Videos"
Language:Python 20aaronstone1699 / Depression-detection
Depression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.
Language:Python 19wywyWang / Depression-Detection-LT-EDI-ACL-2022
Official Implementation for NYCU_TWD LT-EDI@ACL 2022
Language:Python 18AmirHoseein99 / Depression-Engine
Detecting depressed Patient based on Speech Activity, Pauses in Speech and Using Deep learning Approach
Language:Python 17ziqinyeow / depr.ai
Depression web app with text emotion/depression classification and personality/depression test using 4 deep learning models. Demonstrate end-to-end pipeline from training in Python to edge deployment in Typescript
Language:TypeScript 13AnandaRauf / Edison-AT-Emotional-Depression-Assistant-
Edison AT is software Depression Assistant personal.
Language:Python 11faiqali1 / Suicidal-Text-Analysis
Using Machine Learning to predict if text is suicidal.
Language:Jupyter Notebook 11Amey-Thakur / DEPRESSION_DETECTION_USING_TWEETS
Twitter Depression Detection
Language:Jupyter Notebook 10divertingPan / STA-DRN
code for paper 'Spatial-Temporal Attention Network for Depression Recognition from Facial Videos'
Language:Python 10A real time Multimodal Emotion Recognition web app for text, sound and video inputs
Language:Jupyter Notebook 9BouzidiImen / Social_media_Prediction_depression
This consists in using a variety of social networks data, including both images and texts, to detect early signs of depression.
Language:Jupyter Notebook 9chanapapan / Depression-Detection
Comparing Selective Masking Methods for Depression Detection in Social Media
Language:Python 9aksharbhayani / ProjectX
A mobile application to detect the depression level in patients by facial and Twitter analysis.
Language:Dart 6Ritabrata04 / Hybrid-Approach-To-Depression-Detection
This repository applies Deep Learning techniques for depression detection in text, using LSTM, GRU, BiLSTM, BERT models, and a baseline FFNN. It also includes data visualizations, autoencoder semantics, KMeans clustering, and detailed performance comparisons.
Language:Python 6- Language:Java 5
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philiagbo / MachineLearningToClassifyDepressedPatiences
My final year dissertation project. This project takes motor activity data from a control group and a condition group. The data is filtered, cleaned and transformed for appropriate use to find the "best" classification algorithm to identify depressed patients from non-depressed patients
Language:Jupyter Notebook 4NM001007 / An-Attention-based-Hybrid-Suicide-Ideation-Detection
This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.
Language:Jupyter Notebook 3thalia-huynh / predicting-depression-using-nlp
Predicting depression using Twitter posts
Language:Jupyter Notebook 3theatina / Stress_Detection
M.Sc. mini project for NLP class (M908)
Language:Python 3VGandhi27 / Depression-Detection-using-Naive-Bayes
According to the World Health Organization, depression is the leading cause of disability worldwide. Globally, more than 300 million people of all ages suffer from the disorder. And the incidence of the disorder is increasing everywhere. Depression is a complex condition, involving many systems of the body
Language:Jupyter Notebook 3ArfaNada / PFE-Project
A trained machine learning model to detect early symptoms of depression using data collected from X (Twitter) that is integrated into Telegram.
Language:Python 2Blacksharkfin / Depression-suicidal-tendencies-in-tweets
Detection of depression and suicidal tendencies from tweets using sentiment analysis.
Language:Jupyter Notebook 2OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
kishanmaharaj / Actigraphy-based-Depression-Analysis
Predicting depression from daily gross motor activity
Language:Jupyter Notebook 2restradap001 / Depression-in-Mexico
Instrumento para la detección de la depresión en jóvenes mexicanos
Language:Jupyter Notebook 2shubhamjainjnsb / Anti_depression_ROBOT
Here we aim to develop a software plus hardware that uses AI based algorithms to determine if the user is under any sort of physical/mental/emotional trauma and thus under any sort of depression. The bot is capable of generating an report for the user and also alerts his/her care-taker in case of threats to life and severe symptoms of depression using the GSM module. Also using the camera module the Chatbot is capable of deterring the mood of the user using facial expressions. The chatbot is very interactive with the user and can perform tasks such as setting alarms, remainders, to-do-lists etc. The chatbot is integrated into Raspberry Pi3 and thus converted into a mobile robot which follows its user and then interacts . The robot is fitted with sensors to detect fire, smoke and gas in case of emergencies. And Our research shows that by using this chat-bot the level of depression of user decreases gradually.
Language:Python 2Nihalahamad1905 / DEPRESSION-DETECTION-USING-MACHINE-LEARNING
Depression detection using machine learning is a vital area of research given the global burden of mental health disorders. This project explores two primary methodologies: leveraging depression quiz tests and analyzing sentences.
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