Topic: depression-detection Goto Github
Some thing interesting about depression-detection
Some thing interesting about depression-detection
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.
User: aaronstone1699
Home Page: https://parkaidrm.wordpress.com
depression-detection,A mobile application to detect the depression level in patients by facial and Twitter analysis.
User: aksharbhayani
depression-detection,Speech-based diagnosis of depression
User: akshat2430
depression-detection,Twitter Depression Detection
User: amey-thakur
Home Page: https://github.com/Amey-Thakur/DEPRESSION_DETECTION_USING_TWEETS
depression-detection,Detecting depressed Patient based on Speech Activity, Pauses in Speech and Using Deep learning Approach
User: amirhoseein99
depression-detection,Depression Asisstant can help you give solution. Please using Python version 3.9.5 for contribute.
User: anandarauf
depression-detection,Edison AT is software Depression Assistant personal.
User: anandarauf
depression-detection,A trained machine learning model to detect early symptoms of depression using data collected from X (Twitter) that is integrated into Telegram.
User: arfanada
depression-detection,DAEB-τSS3: Imbalanced Social Media Text Depression Detection Method
User: beizhi23
depression-detection,Detection of depression and suicidal tendencies from tweets using sentiment analysis.
User: blacksharkfin
depression-detection,This consists in using a variety of social networks data, including both images and texts, to detect early signs of depression.
User: bouzidiimen
depression-detection,Comparing Selective Masking Methods for Depression Detection in Social Media
User: chanapapan
depression-detection,Official source code for the paper: "Reading Between the Frames Multi-Modal Non-Verbal Depression Detection in Videos"
User: cosmaadrian
depression-detection,Official source code for the paper: "It’s Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers"
User: cosmaadrian
Home Page: https://link.springer.com/chapter/10.1007/978-3-031-28244-7_13
depression-detection,Language model capable of detecting emerging topics from Reddit posts with depression as main theme using the Latent Dirichlet Allocation (LDA) method.
User: cristinaa23
depression-detection,OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
User: divertingpan
depression-detection,code for paper 'Spatial-Temporal Attention Network for Depression Recognition from Facial Videos'
User: divertingpan
depression-detection,depression detection by using tweets
User: eddieir
depression-detection,Using Machine Learning to predict if text is suicidal.
User: faiqali1
Home Page: https://share.streamlit.io/faiqali1/suicidal-text-analysis/main/stream.py
depression-detection,This is an academic final year individual project, published by Liew Jun Yen - Data Analyst
User: gabby-01
depression-detection,Mentoso : Mental health detection and Counselling application
User: hamza2306
Home Page: https://mentoso.online/
depression-detection,Detecting Anxiety and Depression using facial emotion recognition and speech emotion recognition. Written in pythonPython
User: hariharitha21
depression-detection,Predicting depression from daily gross motor activity
User: kishanmaharaj
depression-detection,Undergraduate Project - Application of Monitoring and Recording Depression Emotions
User: mengsiwei
depression-detection,Prompt: والحرب تجعل كل شيءٍ واضحٍ .... 🖊 Output: No depression e` Propability of 99.9% 🧠⏳
User: mo-shaeerah
Home Page: https://mo-shaeerah-depression-prediction-based-on-text.streamlit.app/
depression-detection,Depression Calculator as a Final Project for Datamining Subject in the College
User: nafishandoko
Home Page: https://depr-calc.nafishandoko.repl.co/
depression-detection,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.
User: nihalahamad1905
depression-detection,A mental health quiz app to help individuals check in with themselves.
User: nitrotap
depression-detection,This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.
User: nm001007
depression-detection,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
User: philiagbo
depression-detection,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.
User: rafalposwiata
Home Page: https://aclanthology.org/2022.ltedi-1.40/
depression-detection,Extract explainablity from RoBERTa 🪆 ad Born 🐈 while classifying depresson 🎭
User: ranieri-unimi
depression-detection,Instrumento para la detección de la depresión en jóvenes mexicanos
User: restradap001
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.
User: ritabrata04
depression-detection,
User: sahasourav17
Home Page: https://www.kaggle.com/datasets/sahasourav17/students-anxiety-and-depression-dataset
depression-detection,A Bidirectional LSTM model is built to detect depressive tweets. This model is also compared with other models like GRU and LSTM
User: saket0510
depression-detection,Identifying depression markers via social media and building an early-stage recommendation engine
User: sayantikabanik
depression-detection,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.
User: shubhamjainjnsb
depression-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.
User: sidmulajkar
depression-detection,This repository contains the contents of a Master's degree in Cognitive Science thesis project concerned with assessing the generalizability of machine learning models for depression detection in transcribed clinical interviews with patients diagnosed with chronic and first-episode major depressive disorder (MDD).
User: sofieditmer
depression-detection,Predicting depression using Twitter posts
User: thalia-huynh
depression-detection,M.Sc. mini project for NLP class (M908)
User: theatina
depression-detection,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
User: vgandhi27
depression-detection,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.
User: vibhuarvind
Home Page: https://datasets.simula.no/depresjon/
depression-detection,Official Implementation for NYCU_TWD LT-EDI@ACL 2022
User: wywywang
depression-detection,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
User: ziqinyeow
Home Page: https://deprai.vercel.app/
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