By Dr Rohitash Chandra

Conference Papers

Chandra R; Wong G, 2015, 'Competitive two-island cooperative co-evolution for training feedforward neural networks for pattern classification problems', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2015.7280349

Chandra R; Bali K, 2015, 'Competitive two-island cooperative coevolution for real parameter global optimisation', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 93 - 100, http://dx.doi.org/10.1109/CEC.2015.7256879

Chandra R; Dayal K, 2015, 'Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 1784 - 1791, http://dx.doi.org/10.1109/CEC.2015.7257103

Chandra R, 2015, 'Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction', in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, pp. 101 - 108, http://dx.doi.org/10.1109/CEC.2015.7256880

Chandra R, 2014, 'Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 565 - 572, http://dx.doi.org/10.1109/IJCNN.2014.6889421

Chand S; Chandra R, 2014, 'Cooperative coevolution of feed forward neural networks for financial time series problem', in Proceedings of the International Joint Conference on Neural Networks, pp. 202 - 209, http://dx.doi.org/10.1109/IJCNN.2014.6889568

Chand S; Chandra R, 2014, 'Multi-objective cooperative coevolution of neural networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 190 - 197, http://dx.doi.org/10.1109/IJCNN.2014.6889442

Singh V; Bali A; Adhikthikar A; Chandra R, 2014, 'Web and mobile based tourist travel guide system for Fiji's tourism industry', in Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2014, http://dx.doi.org/10.1109/APWCCSE.2014.7053840

Chandra R, 2013, 'Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2013.6706997

Chandra R; Frean M; Zhang M, 2011, 'A memetic framework for cooperative coevolution of recurrent neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 673 - 680, http://dx.doi.org/10.1109/IJCNN.2011.6033286

Chandra R; Frean M; Zhang M, 2011, 'Modularity adaptation in cooperative coevolution of feedforward neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 681 - 688, http://dx.doi.org/10.1109/IJCNN.2011.6033287

Rolland L; Chandra R, 2010, 'On solving the forward kinematics of the 6-6 general parallel manipulator with an efficient evolutionary algorithm', pp. 117 - 124, http://dx.doi.org/10.1007/978-3-7091-0277-0_13

Chandra R; Frean M; Rolland L, 2009, 'A meta-heuristic paradigm for solving the forward kinematics of 6-6 general parallel manipulator', in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, pp. 171 - 176, http://dx.doi.org/10.1109/CIRA.2009.5423212

Chandra R; Zhang M; Rolland L, 2009, 'Solving the Forward Kinematics of the 3RPR Planar Parallel Manipulator using a Hybrid Meta-Heuristic Paradigm', Institute of Electrical and Electronics Engineers (IEEE), pp. 1 - 6, presented at 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA), http://dx.doi.org/10.1109/cira.2009.5423213

Rolland L; Chandra R, 2009, 'Forward kinematics of the 3RPR planar parallel manipulators using real coded Genetic Algorithms', in 2009 24th International Symposium on Computer and Information Sciences, ISCIS 2009, pp. 381 - 386, http://dx.doi.org/10.1109/ISCIS.2009.5291810

Rolland L; Chandra R, 2009, 'Forward kinematics of the 6-6 general parallel manipulator using real coded genetic algorithms', in IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 1637 - 1642, http://dx.doi.org/10.1109/AIM.2009.5229824

Chandra R; Omlin CW, 2008, 'Hybrid evolutionary one-step gradient descent for training recurrent neural networks', in Proceedings of the 2008 International Conference on Genetic and Evolutionary Methods, GEM 2008, pp. 305 - 311

Chandra R; Omlin CW, 2007, 'A hybrid recurrent neural networks architecture inspired by hidden Markov models: Training and extraction of deterministic finite automaton', in International Conference on Artificial Intelligence and Pattern Recognition 2007, AIPR 2007, pp. 278 - 285

Chandra R; Omlin CW, 2007, 'The comparison and combination of genetic and gradient descent learning in recurrent neural networks: An application to speech phoneme classification', in International Conference on Artificial Intelligence and Pattern Recognition 2007, AIPR 2007, pp. 286 - 293

Chandra R; Omlin CW, 2006, 'Training and extraction of fuzzy finite state automata in recurrent neural networks', in Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, pp. 271 - 275

Conference Presentations

Chandra R; Azam D; Dietmar Müller R, 2019, 'Probabilistic modelling of sedimentary basin evolution using Bayeslands', http://dx.doi.org/10.1080/22020586.2019.12073181

Alac R; Zahirovic S; Salles T; Muller D; Cripps S; Ramos F; Chandra R, 2018, 'Surface Process Models of The Lake Eyre Basin Using Badlands Software', http://dx.doi.org/10.1071/aseg2018abp011

Software / Code

Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2020, 3DWofE: An open-source software package for three-dimensional weights of evidence modeling[Formula presented], Published: 01 November 2020, Software / Code, http://dx.doi.org/10.1016/j.simpa.2020.100039

Media

Venkataraman V; Chandra R, 2022, Artificial Intelligence meets Bhagavad Gita and the Upanishads, India, , https://www.csp.indica.in/artificial-intelligence-meets-bhagavad-gita-and-the-upanishads/

Chandra R; Lande J; Yu C; Kaurav Y, 2022, Global COVID-19 Twitter dataset, Kaggle, , http://dx.doi.org/10.34740/kaggle/ds/2397387

Chandra R, 2022, AI, philosophy and religion: what machine learning can tell us about the Bhagavad Gita, , https://theconversation.com/ai-philosophy-and-religion-what-machine-learning-can-tell-us-about-the-bhagavad-gita-182517

Chandra R, 2021, Travelling through deep time to find copper for a clean energy future, , https://www.stuff.co.nz/environment/climate-news/300357891/travelling-through-deep-time-to-find-copper-for-a-clean-energy-future

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

Back to profile page