By Associate Professor Rohitash Chandra

Preprints

Khan AA; Chaudhari O; Chandra R, 2023, A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation, http://dx.doi.org/10.48550/arxiv.2304.02858

Barve S; Webster JM; Chandra R, 2023, Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing, http://dx.doi.org/10.48550/arxiv.2301.10876

Chandra R; Sharma YV, 2023, Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models, http://dx.doi.org/10.48550/arxiv.2201.06843

Bansal C; Chandra R; Agarwal V; Deepa PR, 2023, A clustering and graph deep learning-based framework for COVID-19 drug repurposing, http://dx.doi.org/10.48550/arxiv.2306.13995

Chandra R; Sonawane J; Lande J; Yu C, 2023, An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines, http://dx.doi.org/10.48550/arxiv.2306.13797

Chandra R; Bansal C; Kang M; Blau T; Agarwal V; Singh P; Wilson LOW; Vasan S, 2023, Unsupervised machine learning framework for discriminating major variants of concern during COVID-19, http://dx.doi.org/10.48550/arxiv.2208.01439

Shukla A; Bansal C; Badhe S; Ranjan M; Chandra R, 2023, An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis, http://dx.doi.org/10.48550/arxiv.2303.07201

Lande J; Pillay A; Chandra R, 2023, Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron, http://dx.doi.org/10.48550/arxiv.2303.00135

Chandra R, 2023, Leadership and Management of Fijian Universities: An Academic Perspective From Australia, http://dx.doi.org/10.2139/ssrn.4369970

Ngo G; Beard R; Chandra R, 2022, Evolutionary bagging for ensemble learning, http://dx.doi.org/10.48550/arxiv.2208.02400

Chandra R; Ranjan M, 2022, Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita, http://dx.doi.org/10.48550/arxiv.2205.11020

Chandra R; Jain M; Maharana M; Krivitsky PN, 2022, Revisiting Bayesian Autoencoders with MCMC, http://dx.doi.org/10.48550/arxiv.2104.05915

Chand S; Rajesh K; Chandra R, 2022, MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem, http://dx.doi.org/10.48550/arxiv.2204.11162

Kapoor A; Negi A; Marshall L; Chandra R, 2022, Cyclone Trajectory and Intensity Prediction with Uncertainty Quantification Using Variational Recurrent Neural Networks, http://dx.doi.org/10.2139/ssrn.4283622

Shirmard H; Farahbakhsh E; Muller RD; Chandra R, 2021, A review of machine learning in processing remote sensing data for mineral exploration, http://dx.doi.org/10.48550/arxiv.2103.07678

Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, Bayesian graph convolutional neural networks via tempered MCMC, http://dx.doi.org/10.48550/arxiv.2104.08438

Chandra R; Krishna A, 2021, COVID-19 sentiment analysis via deep learning during the rise of novel cases, http://dx.doi.org/10.48550/arxiv.2104.10662

Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2021, Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands, http://dx.doi.org/10.48550/arxiv.1805.03696

Chandra R; Goyal S; Gupta R, 2021, Evaluation of deep learning models for multi-step ahead time series prediction, http://dx.doi.org/10.48550/arxiv.2103.14250

Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2020, Three-dimensional weights of evidence modeling of a deep-seated porphyry Cu deposit, http://dx.doi.org/10.48550/arxiv.1910.08162

Chandra R; Azam D; Kapoor A; Müller RD, 2020, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, http://dx.doi.org/10.48550/arxiv.1812.08655

Chandra R; Jain K; Kapoor A; Aman A, 2020, Surrogate-assisted parallel tempering for Bayesian neural learning, http://dx.doi.org/10.48550/arxiv.1811.08687

Pall J; Chandra R; Azam D; Salles T; Webster JM; Scalzo R; Cripps S, 2020, Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics, http://dx.doi.org/10.48550/arxiv.1808.02763

Chandra R; Müller RD; Azam D; Deo R; Butterworth N; Salles T; Cripps S, 2019, Multi-core parallel tempering Bayeslands for basin and landscape evolution, http://dx.doi.org/10.48550/arxiv.1806.10939

Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2018, Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success, http://dx.doi.org/10.48550/arxiv.1812.00318

Chandra R; Jain K; Deo RV; Cripps S, 2018, Langevin-gradient parallel tempering for Bayesian neural learning, http://dx.doi.org/10.48550/arxiv.1811.04343

Farahbakhsh E; Chandra R; Olierook HKH; Scalzo R; Clark C; Reddy SM; Muller RD, 2018, Computer vision-based framework for extracting geological lineaments from optical remote sensing data, http://dx.doi.org/10.48550/arxiv.1810.02320

Chandra R; Ong Y-S; Goh C-K, 2018, Co-evolutionary multi-task learning for dynamic time series prediction, http://dx.doi.org/10.48550/arxiv.1703.01887

Deo RV; Chandra R; Sharma A, 2017, Stacked transfer learning for tropical cyclone intensity prediction, http://dx.doi.org/10.48550/arxiv.1708.06539

Chaudhry S; Chandra R, 2015, Design of a Mobile Face Recognition System for Visually Impaired Persons, http://dx.doi.org/10.48550/arxiv.1502.00756

Abel D; Gavidi B; Rollings N; Chandra R, 2015, Development of an Android Application for an Electronic Medical Record System in an Outpatient Environment for Healthcare in Fiji, http://dx.doi.org/10.48550/arxiv.1503.00810

Reddy E; Kumar S; Rollings N; Chandra R, 2015, Mobile Application for Dengue Fever Monitoring and Tracking via GPS: Case Study for Fiji, http://dx.doi.org/10.48550/arxiv.1503.00814

Zhu Y; Peng Y; Li M; Chandra R, An analysis of university ranking systems using an unsupervised machine learning-based ranking framework, http://dx.doi.org/10.21203/rs.3.rs-10833270/v1

Ren G; Chandra R, Analysis of IMDb movie reviews and ratings using a language model framework, http://dx.doi.org/10.36227/techrxiv.175321775.51796737/v1

Olierook HKH; Scalzo R; Kohn D; Chandra R; Farahbakhsh E; Houseman G; Clark C; Reddy SM; Müller RD, Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models, http://dx.doi.org/10.5194/se-2019-4

Jain HA; Agarwal V; Bansal C; Kumar A; Faheem F; Mohammed M-U-R; Murugesan S; Simpson MM; Karpe AV; Chandra R; MacRaild CA; Styles IK; Peterson AL; Cooper MA; Kirkpatrick CMJ; Shah RM; Palombo EA; Trevaskis NL; Creek DJ; Vasan SS; Initiative OBOTS, CoviRx: A User-Friendly Interface for Systematic Down-Selection of Repurposed Drug Candidates for COVID-19, http://dx.doi.org/10.20944/preprints202209.0323.v1

Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success, http://dx.doi.org/10.5194/gmd-2018-306

Farahbakhsh E; Sharma P; Agrawal A; Chandra R, Evaluation of clustering methods for segmentation of hyperspectral remote sensing data, http://dx.doi.org/10.21203/rs.3.rs-10413878/v1

Chandra R; Azam D; Kapoor A; Mulller RD, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, http://dx.doi.org/10.5194/gmd-2018-315

Lovelock T; Chandra R, Unsupervised Machine Learning Framework for Identification of Spatial Distribution of Minerals on Mars, http://dx.doi.org/10.20944/preprints202507.2285.v1

Other

Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2019, Supplementary material to "Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success", http://dx.doi.org/10.5194/gmd-2018-306-supplement

Back to profile page