By Dr Rohitash Chandra

Preprints

Nagar S; Farahbakhsh E; Awange J; Chandra R, 2024, Remote sensing framework for geological mapping via stacked autoencoders and clustering, , http://arxiv.org/abs/2404.02180v1

Vora M; Blau T; Kachhwal V; Solo AMG; Chandra R, 2024, Large language model for Bible sentiment analysis: Sermon on the Mount, , http://arxiv.org/abs/2401.00689v1

Haggerty H; Chandra R, 2024, Self-supervised learning for skin cancer diagnosis with limited training data, , http://arxiv.org/abs/2401.00692v1

Bansal C; Chandra R; Agarwal V; Deepa PR, 2023, A clustering and graph deep learning-based framework for COVID-19 drug repurposing, , http://arxiv.org/abs/2306.13995v1

Chandra R; Sonawane J; Lande J; Yu C, 2023, An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines, , http://arxiv.org/abs/2306.13797v1

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://arxiv.org/abs/2304.02858v3

Chandra R; Chen R; Simmons J, 2023, Bayesian neural networks via MCMC: a Python-based tutorial, , http://arxiv.org/abs/2304.02595v2

Lande J; Pillay A; Chandra R, 2023, Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron, , http://arxiv.org/abs/2303.00135v1

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://arxiv.org/abs/2303.07201v1

Wang T; Beard R; Hawkins J; Chandra R, 2023, Recursive deep learning framework for forecasting the decadal world economic outlook, , http://arxiv.org/abs/2301.10874v1

Barve S; Webster JM; Chandra R, 2023, Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing, , http://arxiv.org/abs/2301.10876v2

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, 2022, 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

Ngo G; Beard R; Chandra R, 2022, Evolutionary bagging for ensemble learning, , http://dx.doi.org/10.1016/j.neucom.2022.08.055

Chandra R; Bansal C; Kang M; Blau T; Agarwal V; Singh P; Wilson LOW; Vasan S, 2022, Unsupervised machine learning framework for discriminating major variants of concern during COVID-19, , http://dx.doi.org/10.1371/journal.pone.0285719

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/10.1371/journal.pone.0273476

Chand S; Rajesh K; Chandra R, 2022, MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem, , http://arxiv.org/abs/2204.11162v1

Chandra R; Sharma YV, 2022, Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models, , http://dx.doi.org/10.1007/s10596-023-10223-4

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; Jain M; Maharana M; Krivitsky PN, 2021, Revisiting Bayesian Autoencoders with MCMC, , http://dx.doi.org/10.48550/arxiv.2104.05915

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; 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

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

Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2019, Three-dimensional weights of evidence modeling of a deep-seated porphyry Cu deposit, , http://dx.doi.org/10.48550/arxiv.1910.08162

Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2019, 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

Chandra R; Azam D; Kapoor A; Mulller RD, 2019, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, , http://dx.doi.org/10.5194/gmd-2018-315

Olierook HKH; Scalzo R; Kohn D; Chandra R; Farahbakhsh E; Houseman G; Clark C; Reddy SM; Müller RD, 2019, 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

Chandra R; Azam D; Kapoor A; Müller RD, 2018, Surrogate-assisted Bayesian inversion for landscape and basin evolution models, , http://dx.doi.org/10.48550/arxiv.1812.08655

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; Kapoor A; Aman A, 2018, Surrogate-assisted parallel tempering for Bayesian neural learning, , http://dx.doi.org/10.48550/arxiv.1811.08687

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

Pall J; Chandra R; Azam D; Salles T; Webster JM; Scalzo R; Cripps S, 2018, 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, 2018, Multi-core parallel tempering Bayeslands for basin and landscape evolution, , http://dx.doi.org/10.48550/arxiv.1806.10939

Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2018, Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands, , http://dx.doi.org/10.48550/arxiv.1805.03696

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

Chandra R; Ong Y-S; Goh C-K, 2017, Co-evolutionary multi-task learning for dynamic time series prediction, , http://dx.doi.org/10.48550/arxiv.1703.01887

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

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

Chandra R, Science and Hinduism Share the Vision of a Quest for Truth, , http://dx.doi.org/10.2139/ssrn.4685559

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