3 citations · 10 across the 18 of their papers we have counts for
19 papers
DynBERG: Dynamic BERT-based Graph neural network for financial fraud detection
Omkar Kulkarni, Rohitash Chandra
Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph…
Capsule Network-Based Multimodal Fusion for Mortgage Risk Assessment from Unstructured Data Sources
Mahsa Tavakoli, Rohitash Chandra, Cristian Bravo
Mortgage risk assessment traditionally relies on structured financial data, which is often proprietary, confidential, and costly. In this study, we propose a novel multimodal deep…
Machine Learning for Detection and Analysis of Novel LLM Jailbreaks
John Hawkins, Aditya Pramar, Rodney Beard +1
Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-cal…
Extreme value forecasting using relevance-based data augmentation with deep learning models
Junru Hua, Rahul Ahluwalia, Rohitash Chandra
Data augmentation with generative adversarial networks (GANs) has been popular for class imbalance problems, mainly for pattern classification and computer vision-related applicati…
Deep learning framework for crater detection and identification on the Moon and Mars
Yihan Ma, Zeyang Yu, Rohitash Chandra
Impact craters are among the most prominent geomorphological features on planetary surfaces and are of substantial significance in planetary science research. Their spatial distrib…
Spatiotemporal deep learning models for detection of rapid intensification in cyclones
Vamshika Sutar, Amandeep Singh, Rohitash Chandra
Cyclone rapid intensification is the rapid increase in cyclone wind intensity, exceeding a threshold of 30 knots, within 24 hours. Rapid intensification is considered an extreme ev…
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses
Rohitash Chandra, Aryan Chaudhari, Yeshwanth Rayavarapu
Large Language models (LLMs) have been prominent for language translation, including low-resource languages. There has been limited study on the assessment of the quality of transl…
Research impact evaluation based on effective authorship contribution sensitivity: h-leadership index
Hardik A. Jain, Rohitash Chandra
The evaluation of a researcher's performance has traditionally relied on various bibliometric measures, with the h-index being one of the most prominent. However, the h-index only…
Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems
Saman Forouzandeh, Pavel N. Krivitsky, Rohitash Chandra
Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant ch…
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
Arun Kumar Selvaraj, Tanay Panat, Rohitash Chandra
The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living and quality of life. It is essent…
A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction
Amuche Ibenegbu, Amandine Schaeffer, Pierre Lafaye de Micheaux +1
Bluebottles (\textit{Physalia} spp.) are marine stingers resembling jellyfish, whose presence on Australian beaches poses a significant public risk due to their venomous nature. Un…
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models
Ashutosh Singh, Rohitash Chandra
During the COVID-19 pandemic, community tensions intensified, contributing to discriminatory sentiments against various religious groups, including Hindu communities. Recent advanc…
Quantile deep learning models for multi-step ahead time series prediction
Jimmy Cheung, Smruthi Rangarajan, Amelia Maddocks +2
Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme valu…
Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
Omkar Kulkarni, Rohitash Chandra
Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantif…
Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis
Xuechun Wang, Rodney Beard, Rohitash Chandra
Machine translation using large language models (LLMs) is having a significant global impact, making communication easier. Mandarin Chinese is the official language used for commun…
A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models
Chen Wang, Rohitash Chandra
The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs)…
Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
Rohitash Chandra, Arpit Kapoor, Siddharth Khedkar +2
In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting…
Large language models for newspaper sentiment analysis during COVID-19: The Guardian
Rohitash Chandra, Baicheng Zhu, Qingying Fang +1
During the COVID-19 pandemic, the news media coverage encompassed a wide range of topics that includes viral transmission, allocation of medical resources, and government response…
Large language model for Bible sentiment analysis: Sermon on the Mount
Mahek Vora, Tom Blau, Vansh Kachhwal +2
The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specif…