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 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 , 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 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 , 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

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

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

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

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, pp. 373 - 373, 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

Kumar AK; Ritam M; Han L; Guo S; Chandra R, 2022, 'Deep learning for predicting respiratory rate from biosignals', Computers in Biology and Medicine, 144, 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, 14, http://dx.doi.org/10.3390/rs14040819

Chandra R; Tiwari A, 2022, 'Distributed Bayesian optimisation framework for deep neuroevolution', Neurocomputing, 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, 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, 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, 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, 10, pp. 21291 - 21315, http://dx.doi.org/10.1109/ACCESS.2022.3152266

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