Posts by Collection

Spatiotemporal Forecasting of Traffic Flow Data using GNN (Ongoing)

In recent times, the development of transport infrastructure has had a great boom, however, traffic problems continue to spread due to an increase in the population in urban areas which ultimately increases pressure on transport networks. Thus, we develop a GNN model (SST-GNN currently) which provides forecasts of traffic flow data such as velocity, etc. Additionally, we introduce DIVE dataset created by collecting traffic data over 20 nodes within the Old Panvel, Maharashtra of the Indian Province. The model achieved an RMSE of 1.76, 1.69 and 1.69 on the DIVE dataset at 15, 30 and 45 mins intervals for prediction.

Classification of ISL using Pose and Object Detection based Techniques

We present two approaches for the classification of Indian Sign Language: (a) Pose-based approach utilizes an LSTM model which takes the skeletal pose landmarks from Mediapipe for a sequence of frames as an input to infer and predict the action. (b) Object detection-based approach utilizes a model built on Scaled-YOLOv4 architecture which performs a frame-by-frame inference. LSTM model achieved an accuracy of around 98% for 8 classes. However, it was found from experimentation that this approach is not very scalable due to the drastic fall in model performance with increase in number of classes. The object detection route allows us to train far more number of classes (about thrice) on the Scaled-YOLOv4 Architecture with only little impact to performance with rise in number of classes. This provides a better and more scalable solution to Sign-to-Text translation with the ability to infer on images, videos and live-cam. The final accuracy of our YOLO model was 95.9% for 25 classes. The notebooks utilized for the project are available on github (link).

Customer Review Sentiment Analysis

The project aims to analyse the sentiment of amazon reviews with the help of Machine Learning & Deep Learning. Sentiment analysis (or opinion mining) uses natural language processing and machine learning to interpret and classify emotions in subjective data. This helps to further understand the overall sentiment of customers as well as predict the sentiments of incoming reviews. It was observed that DT has the lowest scores (0.78) while the rest of the models had a similar accuracy (0.86 - 0.87). Also, it can be seen that the neural network models appear to have similar accuracy (0.85) as that of the simpler ML counterparts however, it must be noted that this accuracy was achieved with half the data than that feeded to ML models. As evident, the neural network models outperform the ML models. Additionally, the models were deployed to Heroku and an application was developed using Flutter to predict sentiment of any input sentence using API calls. The dataset utilized is available on github (link). Our codebase for model creation (link), deployment (link) and flutter application (link) has been made public.

Deep-Learning Spatiotemporal Prediction Framework for Particulate Matter under Dynamic Monitoring

Published in Transportation Research Record: Journal of the Transportation Research Board, 2022

A spatiotemporal prediction of hourly particulate matter with different deep-learning modeling techniques for Delhi, India was performed. The secondary data of particulate matter concentrations and the meteorological parameters for the four static monitors in the area are collected from Central Pollution Control Board (CPCB) for dates between January 2019 and April 2021. Three models with convolutional neural network (CNN), long short-term memory (LSTM), and CNN-LSTM are developed for a total of 15 hexagonal cells. The predictions are accurate for the CNN-LSTM model compared with the values obtained from the static monitor. Also, compared with the existing and individual models, the proposed hybrid CNN-LSTM model performed better for most of the cells.

Mittal, V., Sasetty, S., Choudhary, R., & Agarwal, A. (2022). Deep-Learning Spatiotemporal Prediction Framework for Particulate Matter under Dynamic Monitoring. Transportation Research Record, 2676(8), 56-73. https://journals.sagepub.com/doi/10.1177/03611981221082589

Classification of ISL using Pose and Object Detection based Techniques

In this project, we want to bridge the communication gap for the hearing-impaired and thereby contribute to the development of suitable habitats for them by presenting two approaches for the classification of Indian Sign Language: (a) the object detection-based approach utilizes a model built on Scaled-YOLOv4 architecture which performs a frame-by-frame inference and (b) the Pose-based approach utilizes an LSTM model which takes the skeletal pose landmarks from Mediapipe for a sequence of frames as an input to infer and predict the action. The usage of Mediapipe to collect landmarks enhanced the LSTM module’s accuracy to around 98% for 8 classes. However, it was found from experimentation that this approach is not very scalable due to the drastic fall in model performance with increase in number of classes. The object detection route allows us to train far more number of classes (about thrice) on the Scaled-YOLOv4 Architecture with only little impact to performance with rise in number of classes with a final accuracy of 95.9% for 25 classes.

Accepted in SmartCom 2023.

MIDIVIS: Effective Music Visualization For Exploring And Evaluating Generated Alternatives In Computer-Assisted Composition

MIDI-Vis, a MIDI visualization tool is proposed with two exploratory functions: 1) a MIDIComp view which allows the user to visually compare the symbolic content of MIDI files. 2) MIDICluster, a t-Distributed Stochastic Neighbor Embedding (t-SNE) view to explore MIDI clusters by discriminating useful musical dimensions from MIDI files. A web-based, fully responsive system is developed using P5.js and Node.js. It is tested on MIDI corpus data, including via an integration with Calliope, computer-assisted composition (CAC) system which helps generate batches of musical variations of a given MIDI source file.

To be submitted at NIME.

i-Hub Data Mobility Grant 2022

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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