1 code implementation • 2 Apr 2024 • Sandeep Nagar, Ehsan Farahbakhsh, Joseph Awange, Rohitash Chandra
In this study, we present an unsupervised machine learning framework for processing remote sensing data by utilizing stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units.
1 code implementation • 1 Jan 2024 • Hamish Haggerty, Rohitash Chandra
Our framework is applicable to cancer image classification models in the low-labelled data regime.
1 code implementation • 1 Jan 2024 • Mahek Vora, Tom Blau, Vansh Kachhwal, Ashu M. G. Solo, Rohitash Chandra
Sentiment analysis provides a mechanism to study the emotions expressed in text.
1 code implementation • 24 Jun 2023 • Chaarvi Bansal, Rohitash Chandra, Vinti Agarwal, P. R. Deepa
The dataset consists of 438 drugs, of which 224 are under clinical trials for COVID-19 (category A).
1 code implementation • 23 Jun 2023 • Rohitash Chandra, Jayesh Sonawane, Janhavi Lande, Cathy Yu
Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes.
1 code implementation • 21 Apr 2023 • Mahsa Tavakoli, Rohitash Chandra, Fengrui Tian, Cristián Bravo
In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types.
1 code implementation • 6 Apr 2023 • Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra
Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems.
1 code implementation • 2 Apr 2023 • Rohitash Chandra, Royce Chen, Joshua Simmons
In the past three decades, MCMC sampling methods have faced some challenges in being adapted to larger models (such as in deep learning) and big data problems.
no code implementations • 28 Feb 2023 • Akshat Shukla, Chaarvi Bansal, Sushrut Badhe, Mukul Ranjan, Rohitash Chandra
Sanskrit is known as the mother of languages such as Hindi and an ancient source of the Indo-European group of languages.
1 code implementation • 28 Feb 2023 • Janhavi Lande, Arti Pillay, Rohitash Chandra
Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19.
no code implementations • 26 Jan 2023 • Saharsh Barve, Jody M. Webster, Rohitash Chandra
Our framework compares different clustering methods for reef habitat mapping using remote sensing data.
1 code implementation • 25 Jan 2023 • Tianyi Wang, Rodney Beard, John Hawkins, Rohitash Chandra
Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars.
1 code implementation • 4 Aug 2022 • Giang Ngo, Rodney Beard, Rohitash Chandra
Random forest is a prominent example of bagging with additional features in the learning process.
1 code implementation • 1 Aug 2022 • Rohitash Chandra, Chaarvi Bansal, Mingyue Kang, Tom Blau, Vinti Agarwal, Pranjal Singh, Laurence O. W. Wilson, Seshadri Vasan
This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences.
1 code implementation • 23 May 2022 • Rohitash Chandra, Mukul Ranjan
The Bhagavad Gita is core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with major focus on the philosophy of karma.
1 code implementation • 24 Apr 2022 • Shelvin Chand, Kousik Rajesh, Rohitash Chandra
The resource constrained project scheduling problem (RCPSP) is an NP-Hard combinatorial optimization problem.
no code implementations • 18 Jan 2022 • Rohitash Chandra, Yash Vardhan Sharma
The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in a better understanding of paleoclimate and geomorphology.
1 code implementation • 9 Jan 2022 • Rohitash Chandra, Venkatesh Kulkarni
Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis.
1 code implementation • 6 Aug 2021 • Anuraganand Sharma, Prabhat Kumar Singh, Rohitash Chandra
The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets.
1 code implementation • 17 Apr 2021 • Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, Pavel N. Krivitsky
Bayesian inference provides a principled approach to uncertainty quantification of model parameters for deep learning models.
1 code implementation • 13 Apr 2021 • Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky
Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge.
1 code implementation • 11 Apr 2021 • Animesh Renanse, Alok Sharma, Rohitash Chandra
We demonstrate the performance of this class of memory networks under certain algorithmic learning tasks such as copying and recall and compare it with Matrix RNNs.
no code implementations • 5 Apr 2021 • Rohitash Chandra, Aswin Krishna
In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India.
1 code implementation • 26 Mar 2021 • Rohitash Chandra, Shaurya Goyal, Rishabh Gupta
The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks.
Ranked #1 on Time Series Prediction on Sunspot (using extra training data)
no code implementations • 13 Mar 2021 • Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Muller, Rohitash Chandra
As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits.
1 code implementation • 21 Feb 2021 • Animesh Tiwari, Rishabh Gupta, Rohitash Chandra
Air pollution has a wide range of implications on agriculture, economy, road accidents, and health.
1 code implementation • 28 Jan 2021 • Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan
Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences.
2 code implementations • 12 Dec 2018 • Rohitash Chandra, Danial Azam, Arpit Kapoor, R. Dietmar Müller
In this paper, we apply surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model by estimating the likelihood function from the model.
no code implementations • 2 Dec 2018 • Richard Scalzo, David Kohn, Hugo Olierook, Gregory Houseman, Rohitash Chandra, Mark Girolami, Sally Cripps
We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty, using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study.
2 code implementations • 21 Nov 2018 • Rohitash Chandra, Konark Jain, Arpit Kapoor, Ashray Aman
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data.
1 code implementation • 11 Nov 2018 • Rohitash Chandra, Konark Jain, Ratneel V. Deo, Sally Cripps
This not only provides point estimates of optimal set of weights but also the ability to quantify uncertainty in decision making using the posterior distribution.
1 code implementation • 4 Oct 2018 • Ehsan Farahbakhsh, Rohitash Chandra, Hugo K. H. Olierook, Richard Scalzo, Chris Clark, Steven M. Reddy, R. Dietmar Muller
We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data.
2 code implementations • 23 Jun 2018 • Rohitash Chandra, R. Dietmar Müller, Ratneel Deo, Nathaniel Butterworth, Tristan Salles, Sally Cripps
The results show that PT in Bayeslands not only reduces the computation time over a multi-core architecture, but also provides a means to improve the sampling process in a multi-modal landscape.
Geophysics Distributed, Parallel, and Cluster Computing
1 code implementation • 2 May 2018 • Rohitash Chandra, Danial Azam, R. Dietmar Müller, Tristan Salles, Sally Cripps
The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model.
no code implementations • 22 Aug 2017 • Ratneel Vikash Deo, Rohitash Chandra, Anuraganand Sharma
In this paper, we employ transfer stacking as a means of studying the effects of cyclones whereby we evaluate if cyclones in different geographic locations can be helpful for improving generalization performs.
1 code implementation • 27 Feb 2017 • Rohitash Chandra, Yew-Soon Ong, Chi-Keong Goh
In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction.
no code implementations • 17 Jan 2017 • Rohitash Chandra
A class imbalanced problem is encountered which makes it very challenging to achieve promising performance.
no code implementations • 2 Jan 2017 • Rohitash Chandra
In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations.
no code implementations • 3 Feb 2015 • Shonal Chaudhry, Rohitash Chandra
It is estimated that 285 million people globally are visually impaired.