Rohitash Chandra

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Mengjie Zhang，FRSNZ, FEngNZ, FIE...Professor of Computer Science, Victoria University of WellingtonVerified email at ecs.vuw.ac.nz
Ehsan FarahbakhshEarthByte Group, School of Geosciences, University of SydneyVerified email at sydney.edu.au
Dietmar MüllerProfessor of Geophysics, EarthByte Group, Univ. of SydneyVerified email at sydney.edu.au
Marcus FreanVictoria University of WellingtonVerified email at ecs.vuw.ac.nz
Sally (Wood) CrippsUniversity of Technology SydneyVerified email at uts.edu.au
Luc RollandAvailable for consultancyVerified email at uws.ac.uk
Ratneel DeoPhD ScholarVerified email at sydney.edu.au
Christian OmlinUniversity of Agder, NorwayVerified email at uia.no
Yew-Soon OngPresident Chair Professor of Computer Science, A*Star AI Chief Scientist, FIEEEVerified email at ntu.edu.sg
Richard ScalzoCSIROVerified email at data61.csiro.au
Hojat ShirmardPhD Candidate at the University of SydneyVerified email at ut.ac.ir
Arpit KapoorPhD Candidate, UNSW SydneyVerified email at unsw.edu.au
Dr. Anuraganand SharmaThe University of the South Pacific, Fiji and University of Canberra, AustraliaVerified email at usp.ac.fj
Hugo OlierookSenior Research Fellow in Geology, Curtin UniversityVerified email at curtin.edu.au
Tristan SallesSchool of Geosciences, The University of SydneyVerified email at sydney.edu.au
Danial AzamUniversity of SydneyVerified email at sydney.edu.au
Steven ReddyProfessor of Geology, Curtin UniversityVerified email at curtin.edu.au
Chris ClarkProfessor of Geology, Curtin UniversityVerified email at curtin.edu.au
Abhishek GuptaRamanujan Faculty Fellow, IIT Goa; (formerly Scientist at A*STAR and NTU Singapore)Verified email at iitgoa.ac.in
Kavitesh Kumar BaliCentre for Frontier AI Research (CFAR), A*STAR. University of the South Pacific, Fiji.Verified email at cfar.a-star.edu.sg

Rohitash Chandra

UNSW

Verified email at unsw.edu.au - Homepage

Bayesian deep learning Neuroevolution Climate Extremes Language Models Comparative Religion


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction R Chandra, M Zhang Neurocomputing 86, 116-123, 2012	280	2012
A review of machine learning in processing remote sensing data for mineral exploration H Shirmard, E Farahbakhsh, D Muller, R Chandra Remote Sensing of Environment 268, 112750, 2022	211	2022
Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction R Chandra IEEE Transactions on Neural Networks and Learning Systems, 2015	182	2015
Evaluation of deep learning models for multi-step ahead time series prediction R Chandra, S Goyal, R Gupta IEEE Access 9, 83105-83123, 2021	178	2021
Deep learning via LSTM models for COVID-19 infection forecasting in India R Chandra, A Jain, DS Chauhan PLOS One 17 (1), e0262708, 2022	164	2022
COVID-19 sentiment analysis via deep learning during the rise of novel cases R Chandra, A Krishna PloS One 16 (8), e0255615, 2021	123	2021
Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction R Chandra, YS Ong, CK Goh Neurocomputing 243, 21-34, 2017	96	2017
Evolutionary bagging for ensemble learning G Ngo, R Beard, R Chandra Neurocomputing, 2022	91	2022
SMOTified-GAN for class imbalanced pattern classification problems A Sharma, PK Singh, R Chandra IEEE Access, 2022	68	2022
Deep learning for predicting respiratory rate from biosignals AK Kumar, M Ritam, L Han, S Guo, R Chandra Computers in biology and medicine 144, 105338, 2022	63	2022
Integration of selective dimensionality reduction techniques for mineral exploration using ASTER satellite data H Shirmard, E Farahbakhsh, A Beiranvand Pour, AM Muslim, RD Müller, ... Remote Sensing 12 (8), 1261, 2020	59	2020
Computer vision-based framework for extracting geological lineaments from optical remote sensing data E Farahbakhsh, R Chandra, HKH Olierook, R Scalzo, C Clark, SM Reddy, ... International Journal of Remote Sensing, 2020	59	2020
Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance R Chandra, S Chand Applied Soft Computing 49, 462-473, 2016	59	2016
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation AA Khan, O Chaudhari, R Chandra Expert Systems with Applications, 2024	53	2024
A comparative study of convolutional neural networks and conventional machine learning models for lithological mapping using remote sensing data H Shirmard, E Farahbakhsh, E Heidari, A Beiranvand Pour, B Pradhan, ... Remote Sensing 14 (4), 819, 2022	53	2022
Co-evolutionary multi-task learning for dynamic time series prediction R Chandra, YS Ong, CK Goh Applied Soft Computing 70, 576-589, 2018	53	2018
Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic R Chandra, Y He PloS One 16 (7), e0253217, 2021	52	2021
Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models HKH Olierook, R Scalzo, D Kohn, R Chandra, E Farahbakhsh, ... Geoscience Frontiers, 2021	52	2021
Evolutionary multi-task learning for modular knowledge representation in neural networks R Chandra, A Gupta, YS Ong, CK Goh Neural Processing Letters 47 (3), 993-1009, 2018	49	2018
Evolutionary multi-task learning for modular training of feedforward neural networks R Chandra, A Gupta, YS Ong, CK Goh Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016	47	2016

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