By Dr Rohitash Chandra

Book Chapters

Deo R; Chandra R, 2019, 'Multi-step-ahead Cyclone Intensity Prediction with Bayesian Neural Networks', in , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , pp. 177 - 182, http://dx.doi.org/10.1109/CIRA.2009.5423213

Journal articles

Deo R; John CM; Zhang C; Whitton K; Salles T; Webster JM; Chandra R, 2024, 'Deepdive: Leveraging Pre-trained Deep Learning for Deep-Sea ROV Biota Identification in the Great Barrier Reef', Scientific Data, 11, http://dx.doi.org/10.1038/s41597-024-03766-3

Nagar S; Farahbakhsh E; Awange J; Chandra R, 2024, 'Remote sensing framework for geological mapping via stacked autoencoders and clustering', Advances in Space Research, 74, pp. 4502 - 4516, http://dx.doi.org/10.1016/j.asr.2024.09.013

Bansal C; Deepa PR; Agarwal V; Chandra R, 2024, 'A clustering and graph deep learning-based framework for COVID-19 drug repurposing', Expert Systems with Applications, 249, http://dx.doi.org/10.1016/j.eswa.2024.123560

Chen E; Andersen MS; Chandra R, 2024, 'Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels', Environmental Modelling and Software, 178, http://dx.doi.org/10.1016/j.envsoft.2024.106072

Khan AA; Chaudhari O; Chandra R, 2024, 'A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation', Expert Systems with Applications, 244, http://dx.doi.org/10.1016/j.eswa.2023.122778

Ke Y; Bian R; Chandra R, 2024, 'A unified machine learning framework for basketball team roster construction: NBA and WNBA[Formula presented]', Applied Soft Computing, 153, http://dx.doi.org/10.1016/j.asoc.2024.111298

Nguyen NM; Tran MN; Chandra R, 2024, 'Sequential reversible jump MCMC for dynamic Bayesian neural networks', Neurocomputing, 564, http://dx.doi.org/10.1016/j.neucom.2023.126960

Khan AA; Hussain S; Chandra R, 2024, 'A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization', Algorithms, 17, http://dx.doi.org/10.3390/a17010033

Chandra R; Simmons J, 2024, 'Bayesian Neural Networks via MCMC: A Python-Based Tutorial', IEEE Access, 12, pp. 70519 - 70549, http://dx.doi.org/10.1109/ACCESS.2024.3401234

Chandra R; Tiwari A; Jain N; Badhe S, 2024, 'Large Language Models for Metaphor Detection: Bhagavad Gita and Sermon on the Mount', IEEE Access, 12, pp. 84452 - 84469, http://dx.doi.org/10.1109/ACCESS.2024.3411060

Wang T; Beard R; Hawkins J; Chandra R, 2024, 'Recursive Deep Learning Framework for Forecasting the Decadal World Economic Outlook', IEEE Access, 12, pp. 152921 - 152944, http://dx.doi.org/10.1109/ACCESS.2024.3472859

Deo R; Webster JM; Salles T; Chandra R, 2024, 'ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores', IEEE Access, 12, pp. 12164 - 12180, http://dx.doi.org/10.1109/ACCESS.2023.3341156

Chandra R, 2024, 'Science and Hinduism share the vision of a quest for truth', Nature Human Behaviour, http://dx.doi.org/10.1038/s41562-024-02055-8

Renanse A; Sharma A; Chandra R, 2023, 'Memory capacity of recurrent neural networks with matrix representation', Neurocomputing, 560, http://dx.doi.org/10.1016/j.neucom.2023.126824

Chandra R; Sharma YV, 2023, 'Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models', Computational Geosciences, 27, pp. 939 - 954, http://dx.doi.org/10.1007/s10596-023-10223-4

Bai G; Chandra R, 2023, 'Gradient boosting Bayesian neural networks via Langevin MCMC', Neurocomputing, 558, http://dx.doi.org/10.1016/j.neucom.2023.126726

Kapoor A; Pathiraja S; Marshall L; Chandra R, 2023, 'DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling', Environmental Modelling and Software, 169, http://dx.doi.org/10.1016/j.envsoft.2023.105831

Lande J; Pillay A; Chandra R, 2023, 'Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron', PLoS ONE, 18, http://dx.doi.org/10.1371/journal.pone.0288681

Barve S; Webster JM; Chandra R, 2023, 'Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing', Information (Switzerland), 14, http://dx.doi.org/10.3390/info14070373

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', PLoS ONE, 18, http://dx.doi.org/10.1371/journal.pone.0285719

Kapoor A; Negi A; Marshall L; Chandra R, 2023, 'Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks', Environmental Modelling and Software, 162, http://dx.doi.org/10.1016/j.envsoft.2023.105654

Kumar AK; Jain S; Jain S; Ritam M; Xia Y; Chandra R, 2023, 'Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system', Computer Methods and Programs in Biomedicine, 231, http://dx.doi.org/10.1016/j.cmpb.2023.107421

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', Natural Language Processing Journal, 4, pp. 100025 - 100025, http://dx.doi.org/10.1016/j.nlp.2023.100025

Kapoor A; Nukala E; Chandra R, 2022, 'Bayesian neuroevolution using distributed swarm optimization and tempered MCMC[Formula presented]', Applied Soft Computing, 129, http://dx.doi.org/10.1016/j.asoc.2022.109528

Jain HA; Agarwal V; Bansal C; Kumar A; Faheem ; Mohammed MUR; 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', Data, 7, http://dx.doi.org/10.3390/data7110164

Ngo G; Beard R; Chandra R, 2022, 'Evolutionary bagging for ensemble learning', Neurocomputing, 510, pp. 1 - 14, http://dx.doi.org/10.1016/j.neucom.2022.08.055

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, 36, pp. 3467 - 3485, http://dx.doi.org/10.1007/s00477-022-02204-3

Chandra R; Ranjan M, 2022, 'Artificial intelligence for topic modelling in Hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita', PLoS ONE, 17, http://dx.doi.org/10.1371/journal.pone.0273476

Back to profile page