By Dr Rohitash Chandra

Book Chapters

Deo R; Chandra R, 2019, 'Multi-step-ahead Cyclone Intensity Prediction with Bayesian Neural Networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Yanuca Island, Fiji, pp. 282 - 295, http://dx.doi.org/10.1007/978-3-030-29911-8_22

Chandra R; Azizi L; Cripps S, 2017, 'Bayesian neural learning via langevin dynamics for chaotic time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 564 - 573, http://dx.doi.org/10.1007/978-3-319-70139-4_57

Chandra R, 2017, 'Co-evolutionary multi-task learning for modular pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 692 - 701, http://dx.doi.org/10.1007/978-3-319-70136-3_73

Chandra R, 2017, 'Dynamic cyclone wind-intensity prediction using co-evolutionary multi-task learning', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 618 - 627, http://dx.doi.org/10.1007/978-3-319-70139-4_63

Chandra R, 2017, 'Multi-task modular backpropagation for feature-based pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 558 - 566, http://dx.doi.org/10.1007/978-3-319-70136-3_59

Chandra R, 2017, 'Towards an affective computational model for machine consciousness', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 897 - 907, http://dx.doi.org/10.1007/978-3-319-70139-4_91

Hussein S; Chandra R, 2016, 'Chaotic feature selection and reconstruction in time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 3 - 11, http://dx.doi.org/10.1007/978-3-319-46675-0_1

Nand R; Chandra R, 2016, 'Coevolutionary feature selection and reconstruction in neuro-evolution for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 285 - 297, http://dx.doi.org/10.1007/978-3-319-28270-1_24

Nand R; Chandra R, 2016, 'Competitive Island cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 160 - 170, http://dx.doi.org/10.1007/978-3-319-28270-1_14

Chandra R; Gupta A; Ong YS; Goh CK, 2016, 'Evolutionary multi-task learning for modular training of feedforward neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 37 - 46, http://dx.doi.org/10.1007/978-3-319-46672-9_5

Wong G; Chandra R; Sharma A, 2016, 'Memetic cooperative neuro-evolution for chaotic time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 299 - 308, http://dx.doi.org/10.1007/978-3-319-46675-0_33

Nand R; Chandra R, 2016, 'Reverse neuron level decomposition for cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 171 - 182, http://dx.doi.org/10.1007/978-3-319-28270-1_15

Chaudhry S; Chandra R, 2016, 'Unconstrained face detection from a mobile source using convolutional neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 567 - 576, http://dx.doi.org/10.1007/978-3-319-46672-9_63

Chandra R; Dayal KS, 2015, 'Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the south pacific region', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 43 - 52, http://dx.doi.org/10.1007/978-3-319-26555-1_6

Bali KK; Chandra R; Omidvar MN, 2015, 'Competitive island-based cooperative coevolution for efficient optimization of large-scale fully-separable continuous functions', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 137 - 147, http://dx.doi.org/10.1007/978-3-319-26555-1_16

Wong G; Chandra R, 2015, 'Enhancing competitive island cooperative neuro-evolution through backpropagation for pattern classification', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 293 - 301, http://dx.doi.org/10.1007/978-3-319-26532-2_32

Bali KK; Chandra R, 2015, 'Multi-island competitive cooperative coevolution for real parameter global optimization', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 127 - 136, http://dx.doi.org/10.1007/978-3-319-26555-1_15

Nand R; Chandra R, 2015, 'Neuron-synapse level problem decomposition method for cooperative neuro-evolution of feedforward networks for time series prediction', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 90 - 100, http://dx.doi.org/10.1007/978-3-319-26555-1_11

Bali KK; Chandra R, 2015, 'Scaling up multi-island competitive cooperative coevolution for real parameter global optimisation', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 34 - 48, http://dx.doi.org/10.1007/978-3-319-26350-2_4

Chandra R; Zhang M; Peng L, 2012, 'Application of cooperative convolution optimization for ¹³C metabolic flux analysis: Simulation of isotopic labeling patterns based on tandem mass spectrometry measurements', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 178 - 187, http://dx.doi.org/10.1007/978-3-642-34859-4_18

Chandra R; Frean M; Zhang M, 2010, 'An encoding scheme for cooperative coevolutionary feedforward neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 253 - 262, http://dx.doi.org/10.1007/978-3-642-17432-2_26

Chandra R; Zhang M; Rolland L, 2009, 'Solving the forward kinematics of the 3RPR planar parallel manipulator using a hybrid meta-heuristic paradigm', in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, pp. 177 - 182, http://dx.doi.org/10.1109/CIRA.2009.5423213

Journal articles

Kumar AK; Ritam M; Han L; Guo S; Chandra R, 2022, 'Deep learning for predicting respiratory rate from biosignals', Computers in Biology and Medicine, vol. 144, pp. 105338 - 105338, http://dx.doi.org/10.1016/j.compbiomed.2022.105338

Shirmard H; Farahbakhsh E; Heidari E; Pour AB; Pradhan B; Müller D; Chandra R, 2022, 'A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data', Remote Sensing, vol. 14, pp. 819 - 819, http://dx.doi.org/10.3390/rs14040819

Chandra R; Tiwari A, 2022, 'Distributed Bayesian optimisation framework for deep neuroevolution', Neurocomputing, vol. 470, pp. 51 - 65, http://dx.doi.org/10.1016/j.neucom.2021.10.045

Shirmard H; Farahbakhsh E; Müller RD; Chandra R, 2022, 'A review of machine learning in processing remote sensing data for mineral exploration', Remote Sensing of Environment, vol. 268, http://dx.doi.org/10.1016/j.rse.2021.112750

Chandra R; Jain A; Chauhan DS, 2022, 'Deep learning via LSTM models for COVID-19 infection forecasting in India', PLoS ONE, vol. 17, http://dx.doi.org/10.1371/journal.pone.0262708

Chandra R; Jain M; Maharana M; Krivitsky PN, 2022, 'Revisiting Bayesian Autoencoders With MCMC', IEEE Access, vol. 10, pp. 40482 - 40495, http://dx.doi.org/10.1109/ACCESS.2022.3163270

Chandra R; Kulkarni V, 2022, 'Semantic and Sentiment Analysis of Selected Bhagavad Gita Translations Using BERT-Based Language Framework', IEEE Access, vol. 10, pp. 21291 - 21315, http://dx.doi.org/10.1109/ACCESS.2022.3152266

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

Anshuka A; Chandra R; Buzacott AJV; Sanderson D; van Ogtrop FF, 2022, 'Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model', Stochastic Environmental Research and Risk Assessment, http://dx.doi.org/10.1007/s00477-022-02204-3

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, vol. 137, pp. 104300 - 104300, 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, vol. 16, pp. e0255615, 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, vol. 16, pp. e0253217, 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, vol. 139, pp. 105002 - 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, vol. 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, vol. 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, vol. 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, vol. 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, vol. 94, 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, vol. 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, vol. 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, vol. 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, vol. 125, pp. 104610 - 104610, http://dx.doi.org/10.1016/j.envsoft.2019.104610

Chandra R; Kapoor A, 2020, 'Bayesian neural multi-source transfer learning', Neurocomputing, vol. 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, vol. 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, vol. 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, vol. 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, vol. 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, vol. 204, pp. 12 - 32, http://dx.doi.org/10.1016/j.gexplo.2019.05.003

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