Posts by Collection



Automated phase classification in cyclic alternating patterns in sleep stages using Wigner–Ville Distribution based features

Published in Computers in Biology and Medicine, 2020

In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner–Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time–frequency analysis of the signals whereas RE provides least time–frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.

Transformer models for classification of COVID19 posts on Twitter

Published in WNUT workshop, EMNLP, 2021

We present a system to identify tweets about the COVID19 disease outbreak that are deemed to be informative on Twitter for use in downstream applications. The system scored a F1-score of 0.8941, Precision of 0.9028, Recall of 0.8856 and Accuracy of 0.9010. In the shared task organized as part of the 6th Workshop of Noisy User-generated Text (WNUT), the system was ranked 18th by F1-score and 13th by Accuracy.

Digital Image Noise Estimation Using DWT Coefficients

Published in IEEE Transactions on Image Processing, 2021

We propose a hybrid Discrete Wavelet Transform (DWT) and edge information removal based algorithm to estimate the strength of Gaussian noise in digital images. The wavelet coefficients corresponding to spatial domain edges are excluded from noise estimate calculation using a Sobel edge detector. The accuracy of the proposed algorithm is further increased using polynomial regression. Parseval’s theorem mathematically validates the proposed algorithm. The performance of the proposed algorithm is evaluated on a standard LIVE image dataset. Benchmarking results show that the proposed algorithm outperforms all other state of the art algorithms by a large margin over a wide range of noise.

Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets

Published in SMM4H Workshop, NAACL-HLT, 2021

We present a system that addresses classic health-related binary classification problems presented in Tasks 1a, 4, and 8 of the 6th edition of Social Media Mining for Health Applications (SMM4H) shared tasks. We developed a system based on RoBERTa (for Task 1a & 4) and BioBERT (for Task 8). Furthermore, we address the challenge of the imbalanced dataset and propose techniques such as undersampling, oversampling, and data augmentation to overcome the imbalanced nature of a given health-related dataset.



ACM-W Summer School on Algorithmic Game Theory

Summer School, Department of Computer Science, IIT Gandhinagar, 2019

I was a volunteer cum teaching assistant at the ACM-W Summer School on Algorithmic Game Theory. The summer school was organized by ACM and Dr. Neeldhara Misra at IIT Gandhinagar from 2th July 2019 - 14th July 2019.

Signals & Systems Course Fall’19

Course, Department of Electronics & Communication Engineering, IIITN, 2019

I was a teaching assistant for the Signals & Systems Course offered by Department of ECE in Fall’19. The course was taught by Dr. Ankit Bhurane, who was the HoD of ECE Department at that time. The duration of the course was 3 months, from 5th Aug 2019 - 04th Dec 2019.