Projects
Cough it
Members:
Darsh Kaushik, Saranga K. Mahanta
Description:
This project takes a deep learning approach to analyze the acoustic dataset containing cough sound recordings of both COVID-19 positive and negative individuals. It proposes a ConvNet model that performs the classification between COVID-19 positive and negative with a notable AUC score of 87.07 on the blind test set provided by the same for unbiased evaluations of the models. The model takes in 15 MFCC features of the sound examples as input and produces the probability score of the classification as output.
Grad-CAM for the skin-mnist dataset for skin lesion diagnosis
Members:
Kavii Suri, Yash Tripathi
Description:
This project uses a convNet model for detection of skin lesion detection and uses Grad-CAM for explaining the prediction of the model. It uses a modified version of the skin-mnist dataset which only contains the classes nv, bkl, mel (around 550 images of each).
Segmented sequence modeling in Indian classical music
Members:
Yash Tripathi, Shuvra Neel Roy
Description:
This paper is published under IEEE Silicon Conference 2022. The idea of learning the domain knowledge using Variational AutoEncoder(VAE) to detect the stated landmark in Indian Classical Music(ICM).
Textual Entailment as an Evaluation Metric for Abstractive Text Summarization
Members:
Kavii Suri, Saranga K. Mahanta, Swagat Bhuyan
Description:
This project is an NLP model which summarizes text using technique Abstract Summarization with the help of Textual Entailment
Emotion Detection in Images of Faces
Members:
Yash Tripathi
Description:
This was a Keras model that does a happy/sad classification of images of faces. This project was made using Convolutional Neural Network under deeplearning.ai specialization on Coursera
Autonomous driving application Car detection
Members:
Yash Tripathi
Description:
This project was a Keras model that detects objects such as cars, buses, pedestrians, etc which are in the image taken by the front camera of the car. This project is made using YOLO algorithm and Convolutional Neural Network in deeplearning.ai specialization on Coursera
RoadMent
Members:
Krishnav Rajbangshi, Md.Faizal Karim
Description:
An end-to-end project based on Image Segmentation. A website was developed, where if a useruploads a satellite image the model will segment out the roads from the images. The U-Net architecture was used for our model and it was a group project. Main role here was to handle the data, build the model and fine tune it accordingly. Model perfomance was based on IoU score and the loss was Dice-Coefficient loss. Training IoUscore was 0.497 and validation IoU score was 0.415. It was the 2nd Best Project from East Zone in Anveshan National Student Research Convention 2021-2022 and was in Anveshan representing NIT Silchar.
Clean/Dirty Road Classifier
Members:
Krishnav Rajbangshi ,Md.Faizal Karim
Description:
Project is based on ConvNets and Transfer Learning, and is used to identify whether a road is clean or dirty. MobileNet was used as our base architecture and the weights are based on imagenNet. Web Scraping techniques were used to download images of both clean and dirty roads from the internet. The results of this model was pretty good, with a training accuracy of 97.1% and validation accuracy of 91.6%
DiagnoAI
Members:
Krish Sharma, Md. Faizal karim, Niyar R barman.
Description:
DiagnoAI is a tool to detect a disease from a text description of the patient's symptoms and daily condition. It is based on a transformer model called BERT, fine-tuned for 24 common diseases.
CalmSpace
Members:
Krish Sharma
Description:
CalmSpace is an end to end sentiment analysis platform where the user can record or upload the recorded files of their emotion and the deep learning model will learn through algorithm like RNN Model Architecture, and will predict the emotion of the voice . The sentiment analysis will be shown through various graph plot in much understandable way
Flood Segementation
Members:
Krish Sharma, Md.Faizal Karim
Description:
Flood segmentation is a deep learning model that segments flooded area from aerial images.