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Depression detection using multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models.
Conventionally depression detection was done through extensive clinical interviews, wherein the subject’s re- sponses are studied by the psychologist to determine his/her mental state. In our model, we try to imbibe this approach by fusing the 3 modalities i.e. word context, audio, and video and predict an output regarding the mental health of the patient. The output is divided into a binary yes/no denoting whether the patient has symptoms of depression. We’ve built a deep learning model that fuses these 3 modalities, assigning them appropriate weights, and thus gives an output.
Depression Detection github.io
Depression detection and severity estimation
Depression Sentiment Prediction Project
Affective Computing project
Multi-modal depression detection
Final Group ProjeDetecting Human Emotions Using Natural Language Processing UCB Data Analytics
2018/2019 HIT - Depression Research
My tutorials for object detection for Detectron2, Mediapipe, VideoPose3d, AlphaPose, Metrabs
Simple android application that help patients to manage their medical reports.
A text analytics project centered on understanding the r/Depression and r/SuicideWatch subreddits
Tensorflow implementation for DilatedRNN
Repository for my paper: Dimensional Speech Emotion Recognition Using Acoustic Features and Word Embeddings using Multitask Learning
Repository for coding Dimensional speech emotion recognition from acoustic and text
Natural Language Processing (NLP) applied to over 200,000 subreddit submissions to correctly identify subset containing suicidality with applications for advancing clinical suicide prevention research.
Automated Semantic Analysis of Discourse Markers
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level - CVPR '19 Workshop on Computer Vision for Global Challenges
Deep Learning for Time Series Classification
Try out deep learning models online on Google Colab
Comparison of frequently used deep learning architectures (LSTM, biLSTM, GRU and CNN) on ten Human Activity Recognition datasets.
DL tackling Massive-MIMO problems
Text pre-processing modules for deep learning (Keras, tensorflow).
End to end human text analysis package, specifically suited for social media and social scientific applications. It is written in Python 3 and developed by the World Well-Being Project at the University of Pennsylvania.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.