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

Kapoor A; Chandra R, 2026, 'QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification', Journal of Hydrology, 664, pp. 134434 - 134434, http://dx.doi.org/10.1016/j.jhydrol.2025.134434

Lovelock T; Chandra R, 2025, 'Unsupervised Machine Learning Framework for Identification of Spatial Distribution of Minerals on Mars', Remote Sensing, 17, pp. 3578 - 3578, http://dx.doi.org/10.3390/rs17213578

Forouzandeh S; Krivitsky PN; Chandra R, 2025, 'Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems', Expert Systems with Applications, 291, http://dx.doi.org/10.1016/j.eswa.2025.128378

Farahbakhsh E; Goel D; Pimparkar D; Müller RD; Chandra R, 2025, 'Convolutional Neural Networks for Mineral Prospecting Through Alteration Mapping with Remote Sensing Data', Pfg Journal of Photogrammetry Remote Sensing and Geoinformation Science, 93, pp. 379 - 400, http://dx.doi.org/10.1007/s41064-025-00344-z

Wang H; Zhi W; Batista G; Chandra R, 2025, 'Pedestrian trajectory prediction using goal-driven and dynamics-based deep learning framework', Expert Systems with Applications, 271, http://dx.doi.org/10.1016/j.eswa.2025.126557

Chandra R; Zhu B; Fang Q; Shinjikashvili E, 2025, 'Large language models for newspaper sentiment analysis during COVID-19: The Guardian', Applied Soft Computing, 171, http://dx.doi.org/10.1016/j.asoc.2025.112743

Tavakoli M; Chandra R; Tian F; Bravo C, 2025, 'Multi-modal deep learning for credit rating prediction using text and numerical data streams', Applied Soft Computing, 171, http://dx.doi.org/10.1016/j.asoc.2025.112771

Chandra R; Chaudhari A; Rayavarapu Y, 2025, 'An Evaluation of LLMs and Google Translate for Translation of Selected Indian Languages via Sentiment and Semantic Analyses', IEEE Access, 13, pp. 122386 - 122407, http://dx.doi.org/10.1109/ACCESS.2025.3585629

Singh A; Chandra R, 2025, 'HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models', IEEE Access, http://dx.doi.org/10.1109/ACCESS.2025.3617514

Chandra R, 2025, 'Science and Hinduism share the vision of a quest for truth', Nature Human Behaviour, 9, pp. 7 - 8, http://dx.doi.org/10.1038/s41562-024-02055-8

Chandra R; Ren G, 2025, 'Longitudinal abuse and sentiment analysis of Hollywood movie dialogues using language models', Machine Learning with Applications, 22, pp. 100749 - 100749, http://dx.doi.org/10.1016/j.mlwa.2025.100749

Chandra R; Sonawane J; Lande J, 2024, 'An Analysis of Vaccine-Related Sentiments on Twitter (X) from Development to Deployment of COVID-19 Vaccines', Big Data and Cognitive Computing, 8, http://dx.doi.org/10.3390/bdcc8120186

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

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

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