By Dr Rohitash Chandra

Journal articles

Sharma A; Singh PK; Chandra R, 2022, 'SMOTified-GAN for Class Imbalanced Pattern Classification Problems', IEEE Access, 10, pp. 30655 - 30665, http://dx.doi.org/10.1109/ACCESS.2022.3158977

Diaz-Rodriguez J; Müller RD; Chandra R, 2021, 'Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model', Ore Geology Reviews, 137, http://dx.doi.org/10.1016/j.oregeorev.2021.104300

Chandra R; Krishna A, 2021, 'COVID-19 sentiment analysis via deep learning during the rise of novel cases', PLoS ONE, 16, http://dx.doi.org/10.1371/journal.pone.0255615

Chandra R; He Y, 2021, 'Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic', PLoS ONE, 16, http://dx.doi.org/10.1371/journal.pone.0253217

Chandra R; Cripps S; Butterworth N; Muller RD, 2021, 'Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning', Environmental Modelling and Software, 139, pp. 105002, http://dx.doi.org/10.1016/j.envsoft.2021.105002

Olierook HKH; Scalzo R; Kohn D; Chandra R; Farahbakhsh E; Clark C; Reddy SM; Müller RD, 2021, 'Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models', Geoscience Frontiers, 12, pp. 479 - 493, http://dx.doi.org/10.1016/j.gsf.2020.04.015

Chandra R; Bhagat A; Maharana M; Krivitsky PN, 2021, 'Bayesian Graph Convolutional Neural Networks via Tempered MCMC', IEEE Access, 9, pp. 130353 - 130365, http://dx.doi.org/10.1109/ACCESS.2021.3111898

Chandra R; Saini R, 2021, 'Biden vs Trump: Modeling US General Elections Using BERT Language Model', IEEE Access, 9, pp. 128494 - 128505, http://dx.doi.org/10.1109/ACCESS.2021.3111035

Chandra R; Goyal S; Gupta R, 2021, 'Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction', IEEE Access, 9, pp. 83105 - 83123, http://dx.doi.org/10.1109/ACCESS.2021.3085085

Chandra R; Jain K; Kapoor A; Aman A, 2020, 'Surrogate-assisted parallel tempering for Bayesian neural learning', Engineering Applications of Artificial Intelligence, 94, pp. 103700, http://dx.doi.org/10.1016/j.engappai.2020.103700

Chandra R; Azam D; Kapoor A; Dietmar Müller R, 2020, 'Surrogate-assisted Bayesian inversion for landscape and basin evolution models', Geoscientific Model Development, 13, pp. 2959 - 2979, http://dx.doi.org/10.5194/gmd-13-2959-2020

Shirmard H; Farahbakhsh E; Pour AB; Muslim AM; Dietmar Müller R; Chandra R, 2020, 'Integration of selective dimensionality reduction techniques for mineral exploration using ASTER satellite data', Remote Sensing, 12, http://dx.doi.org/10.3390/RS12081261

Farahbakhsh E; Chandra R; Olierook HKH; Scalzo R; Clark C; Reddy SM; Müller RD, 2020, 'Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data', International Journal of Remote Sensing, 41, pp. 1760 - 1787, http://dx.doi.org/10.1080/01431161.2019.1674462

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', Environmental Modelling and Software, 125, http://dx.doi.org/10.1016/j.envsoft.2019.104610

Chandra R; Kapoor A, 2020, 'Bayesian neural multi-source transfer learning', Neurocomputing, 378, pp. 54 - 64, http://dx.doi.org/10.1016/j.neucom.2019.10.042

Farahbakhsh E; Hezarkhani A; Eslamkish T; Bahroudi A; Chandra R, 2020, 'Three-dimensional weights of evidence modelling of a deep-seated porphyry cu deposit', Geochemistry: Exploration, Environment, Analysis, 20, pp. 480 - 495, http://dx.doi.org/10.1144/geochem2020-038

Chandra R; Müller RD; Azam D; Deo R; Butterworth N; Salles T; Cripps S, 2019, 'Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution', Geochemistry, Geophysics, Geosystems, 20, pp. 5082 - 5104, http://dx.doi.org/10.1029/2019GC008465

Chandra R; Azam D; Müller RD; Salles T; Cripps S, 2019, 'Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands', Computers and Geosciences, 131, pp. 89 - 101, http://dx.doi.org/10.1016/j.cageo.2019.06.012

Chandra R; Jain K; Deo RV; Cripps S, 2019, 'Langevin-gradient parallel tempering for Bayesian neural learning', Neurocomputing, 359, pp. 315 - 326, http://dx.doi.org/10.1016/j.neucom.2019.05.082

Farahbakhsh E; Chandra R; Eslamkish T; Müller RD, 2019, 'Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization', Journal of Geochemical Exploration, 204, pp. 12 - 32, http://dx.doi.org/10.1016/j.gexplo.2019.05.003

Scalzo R; Kohn D; Olierook H; Houseman G; Chandra R; Girolami M; Cripps S, 2019, 'Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success', Geoscientific Model Development, 12, pp. 2941 - 2960, http://dx.doi.org/10.5194/gmd-12-2941-2019

Chandra R; Cripps S, 2018, 'Coevolutionary multi-task learning for feature-based modular pattern classification', Neurocomputing, 319, pp. 164 - 175, http://dx.doi.org/10.1016/j.neucom.2018.08.011

Chandra R; Ong YS; Goh CK, 2018, 'Co-evolutionary multi-task learning for dynamic time series prediction', Applied Soft Computing Journal, 70, pp. 576 - 589, http://dx.doi.org/10.1016/j.asoc.2018.05.041

Chandra R; Gupta A; Ong YS; Goh CK, 2018, 'Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks', Neural Processing Letters, 47, pp. 993 - 1009, http://dx.doi.org/10.1007/s11063-017-9718-z

Chandra R; Ong YS; Goh CK, 2017, 'Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction', Neurocomputing, 243, pp. 21 - 34, http://dx.doi.org/10.1016/j.neucom.2017.02.065

Chaudhry S; Chandra R, 2017, 'Face detection and recognition in an unconstrained environment for mobile visual assistive system', Applied Soft Computing Journal, 53, pp. 168 - 180, http://dx.doi.org/10.1016/j.asoc.2016.12.035

Chandra R; Chand S, 2016, 'Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance', Applied Soft Computing Journal, 49, pp. 462 - 473, http://dx.doi.org/10.1016/j.asoc.2016.08.029

Rolland L; Chandra R, 2016, 'The forward kinematics of the 6-6 parallel manipulator using an evolutionary algorithm based on generalized generation gap with parent-centric crossover', Robotica, 34, pp. 1 - 22, http://dx.doi.org/10.1017/S0263574714001362

Chandra R, 2015, 'Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction', IEEE Transactions on Neural Networks and Learning Systems, 26, pp. 3123 - 3136, http://dx.doi.org/10.1109/TNNLS.2015.2404823

Chandra R; Rolland L, 2015, 'Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators', Connection Science, 27, pp. 22 - 39, http://dx.doi.org/10.1080/09540091.2014.948385

Chandra R, 2014, 'Memetic cooperative coevolution of Elman recurrent neural networks', Soft Computing, 18, pp. 1549 - 1559, http://dx.doi.org/10.1007/s00500-013-1160-1

Chandra R; Frean M; Zhang M, 2012, 'Crossover-based local search in cooperative co-evolutionary feedforward neural networks', Applied Soft Computing Journal, 12, pp. 2924 - 2932, http://dx.doi.org/10.1016/j.asoc.2012.04.010

Chandra R; Frean M; Zhang M, 2012, 'On the issue of separability for problem decomposition in cooperative neuro-evolution', Neurocomputing, 87, pp. 33 - 40, http://dx.doi.org/10.1016/j.neucom.2012.02.005

Chandra R; Frean M; Zhang M, 2012, 'Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks', Soft Computing, 16, pp. 1009 - 1020, http://dx.doi.org/10.1007/s00500-011-0798-9

Chandra R; Zhang M, 2012, 'Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction', Neurocomputing, 86, pp. 116 - 123, http://dx.doi.org/10.1016/j.neucom.2012.01.014

Chandra R; Frean M; Zhang M; Omlin CW, 2011, 'Encoding subcomponents in cooperative co-evolutionary recurrent neural networks', Neurocomputing, 74, pp. 3223 - 3234, http://dx.doi.org/10.1016/j.neucom.2011.05.003

Chandra R; Rolland L, 2011, 'On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics', Applied Mathematics and Computation, 217, pp. 8997 - 9008, http://dx.doi.org/10.1016/j.amc.2011.03.106

Chandra R; Knight R; Omlin CW, 2009, 'Renosterveld conservation in South Africa: A case study for handling uncertainty in knowledge-based neural networks for environmental management', Journal of Environmental Informatics, 13, pp. 56 - 65, http://dx.doi.org/10.3808/jei.200900140

Conference Papers

Chandra R; Cripps AS, 2018, 'Bayesian Multi-task Learning for Dynamic Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489323

Zhang Y; Chandra R; Gao J, 2018, 'Cyclone Track Prediction with Matrix Neural Networks', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489077

Wong G; Sharma A; Chandra R, 2018, 'Information Collection Strategies in Memetic Cooperative Neuroevolution for Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489184

Chandra R, 2018, 'Multi-Task Modular Backpropagation for Dynamic Time Series Prediction', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2018.8489740

Tan AW; Sagarna R; Gupta A; Chandra R; Ong YS, 2017, 'Coping with Data Scarcity in Aircraft Engine Design', in 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, American Institute of Aeronautics and Astronautics, presented at 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, http://dx.doi.org/10.2514/6.2017-4434

Bali K; Chandra R; Omidvar MN, 2016, 'Contribution based multi-island competitive cooperative coevolution', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 1823 - 1830, http://dx.doi.org/10.1109/CEC.2016.7744010

Rana M; Chandra R; Agelidis VG, 2016, 'Cooperative neuro-evolutionary recurrent neural networks for solar power prediction', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4691 - 4698, http://dx.doi.org/10.1109/CEC.2016.7744389

Hussein S; Chandra R; Sharma A, 2016, 'Multi-step-ahead chaotic time series prediction using coevolutionary recurrent neural networks', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 3084 - 3091, http://dx.doi.org/10.1109/CEC.2016.7744179

Chandra R; Deo R; Bali K; Sharma A, 2016, 'On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4823 - 4830, http://dx.doi.org/10.1109/CEC.2016.7744408

Chandra R; Deo R; Omlin CW, 2016, 'An architecture for encoding two-dimensional cyclone track prediction problem in coevolutionary recurrent neural networks', in Proceedings of the International Joint Conference on Neural Networks, pp. 4865 - 4872, http://dx.doi.org/10.1109/IJCNN.2016.7727839

Deo R; Chandra R, 2016, 'Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction', in Proceedings of the International Joint Conference on Neural Networks, pp. 489 - 496, http://dx.doi.org/10.1109/IJCNN.2016.7727239

Chandra R; Dayal K; Rollings N, 2015, 'Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2015.7280394

Back to profile page