Cough it

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.

Members:

Darsh Kaushik, Saranga K. Mahanta

Grad-CAM for the skin-mnist dataset for skin lesion diagnosis

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).

Members:

Kavii Suri, Yash Tripathi

Segmented sequence modeling in Indian classical music

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).

Members:

Yash Tripathi, Shuvra Neel Roy

Textual Entailment as an Evaluation Metric for Abstractive Text Summarization

This project is an NLP model which summarizes text using technique Abstract Summarization with the help of Textual Entailment.

Members:

Kavii Suri, Saranga K. Mahanta, Swagat Bhuyan

Emotion Detection in Images of Faces

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.

Members:

Yash Tripathi

Autonomous driving application Car detection

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.

Members:

Yash Tripathi

RoadMent

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.

Members:

Krishnav Rajbangshi, Md.Faizal Karim

Clean/Dirty Road Classifier

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%.

Members:

Krishnav Rajbangshi, Md.Faizal Karim

DiagnoAI

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.

Members:

Krish Sharma, Md. Faizal karim, Niyar R barman

CalmSpace

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.

Members:

Krish Sharma

Flood Segmentation

Flood segmentation is a deep learning model that segments flooded area from aerial images.

Members:

Krish Sharma, Md.Faizal Karim