Ritabrata Chakraborty's repositories
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.
SIBYL
Latest Notice: GUI deployment is facing some issues as of 23.01.2024 --- local deployment is still successful from the authors. Should app deployment not be possible from your end,please contact Ritabrata or Rishi for demonstration. To look into our commits details ,please check the wiki of the repository.
Emotion-Recognition-With-Transfer-Learning
This repository contains a work-in-progress emotion recognition project. It employs TensorFlow, the VGG16 pre-trained model, and transfer learning techniques on the FER2013 dataset. Code updates and improvements are forthcoming.
Ensemble-Learning-On-Breast-Cancer-ML-Models
This repository applies multiple machine learning models to the Wisconsin Breast Cancer Dataset, using PCA and GridSearch CV for optimization. It includes hard and soft ensemble voting methods, with future plans for bagging and boosting techniques.
HACKX_CYBERSEC
This repository contains all essential files related to the MUJ HACKX Hackathon.
Heart-Disease-Prediction-Using-KNN-Approach
This project uses the K-Nearest Neighbors (KNN) algorithm to create a heart disease prediction model. It employs a dataset with 14 medical and demographic features, varying the K value between 1 and 21 to optimize accuracy. The model, while educational and informative, is not intended to replace professional medical advice.
INTROSPECT-ELEMENT
A series of miniature projects other than my usual stuff.
tips
:fire: [ECCV 2022] TIPS: Text-Induced Pose Synthesis (official code).
ritabrata
Just a plain, simple and elegant one-page theme for research/academia.
Ritabrata04
Config files for my GitHub profile.
straug
Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.