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Rohitash Chandra
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2020 – today
- 2024
- [j53]Ratneel Deo
, Jody M. Webster, Tristan Salles
ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores. IEEE Access 12: 12164-12180 (2024) - [j52]Rohitash Chandra
Bayesian Neural Networks via MCMC: A Python-Based Tutorial. IEEE Access 12: 70519-70549 (2024) - [j51]Rohitash Chandra
Large Language Models for Metaphor Detection: Bhagavad Gita and Sermon on the Mount. IEEE Access 12: 84452-84469 (2024) - [j50]Tianyi Wang, Rodney Beard
Recursive Deep Learning Framework for Forecasting the Decadal World Economic Outlook. IEEE Access 12: 152921-152944 (2024) - [j49]Azal Ahmad Khan
A Quantum-Inspired Predator-Prey Algorithm for Real-Parameter Optimization. Algorithms 17(1): 33 (2024) - [j48]Yuhao Ke, Ranran Bian
A unified machine learning framework for basketball team roster construction: NBA and WNBA. Appl. Soft Comput. 153: 111298 (2024) - [j47]Eric Chen, Martin S. Andersen, Rohitash Chandra
Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels. Environ. Model. Softw. 178: 106072 (2024) - [j46]Azal Ahmad Khan
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Syst. Appl. 244: 122778 (2024) - [j45]Chaarvi Bansal
A clustering and graph deep learning-based framework for COVID-19 drug repurposing. Expert Syst. Appl. 249: 123560 (2024) - [j44]Nhat Minh Nguyen, Minh-Ngoc Tran, Rohitash Chandra
Sequential reversible jump MCMC for dynamic Bayesian neural networks. Neurocomputing 564: 126960 (2024) - [c52]Honghui Wang, Weiming Zhi, Gustavo Batista, Rohitash Chandra:
Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning. ICRA 2024: 15068-15075 - [i49]Mahek Vora, Tom Blau, Vansh Kachhwal, Ashu M. G. Solo, Rohitash Chandra:
Large language model for Bible sentiment analysis: Sermon on the Mount. CoRR abs/2401.00689 (2024) - [i48]Hamish Haggerty, Rohitash Chandra:
Self-supervised learning for skin cancer diagnosis with limited training data. CoRR abs/2401.00692 (2024) - [i47]Sandeep Nagar, Ehsan Farahbakhsh, Joseph L. Awange, Rohitash Chandra:
Remote sensing framework for geological mapping via stacked autoencoders and clustering. CoRR abs/2404.02180 (2024) - [i46]Rohitash Chandra, Baicheng Zhu, Qingying Fang, Eka Shinjikashvili:
Large language models for sentiment analysis of newspaper articles during COVID-19: The Guardian. CoRR abs/2405.13056 (2024) - [i45]Siddharth Khedkar, R. Willem Vervoort, Rohitash Chandra:
Evaluation of deep learning models for Australian climate extremes: prediction of streamflow and floods. CoRR abs/2407.15882 (2024) - [i44]Chen Wang, Rohitash Chandra:
A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models. CoRR abs/2408.16942 (2024) - [i43]Xuechun Wang, Rodney Beard
Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis. CoRR abs/2409.04964 (2024) - [i42]Omkar Kulkarni, Rohitash Chandra:
Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation. CoRR abs/2410.01847 (2024) - 2023
- [j43]Amit Krishan Kumar
Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system. Comput. Methods Programs Biomed. 231: 107421 (2023) - [j42]Arpit Kapoor
Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks. Environ. Model. Softw. 162: 105654 (2023) - [j41]Arpit Kapoor
DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling. Environ. Model. Softw. 169: 105831 (2023) - [j40]George Bai, Rohitash Chandra
Gradient boosting Bayesian neural networks via Langevin MCMC. Neurocomputing 558: 126726 (2023) - [j39]Animesh Renanse, Alok Sharma, Rohitash Chandra
Memory capacity of recurrent neural networks with matrix representation. Neurocomputing 560: 126824 (2023) - [j38]Saharsh Barve, Jody M. Webster, Rohitash Chandra
Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing. Inf. 14(7): 373 (2023) - [j37]Akshat Shukla, Chaarvi Bansal, Sushrut Badhe, Mukul Ranjan
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis. Nat. Lang. Process. J. 4: 100025 (2023) - [i41]Tianyi Wang, Rodney Beard, John Hawkins, Rohitash Chandra:
Recursive deep learning framework for forecasting the decadal world economic outlook. CoRR abs/2301.10874 (2023) - [i40]Saharsh Barve, Jody M. Webster, Rohitash Chandra:
Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing. CoRR abs/2301.10876 (2023) - [i39]Janhavi Lande, Arti Pillay, Rohitash Chandra:
Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron. CoRR abs/2303.00135 (2023) - [i38]Akshat Shukla, Chaarvi Bansal, Sushrut Badhe, Mukul Ranjan
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis. CoRR abs/2303.07201 (2023) - [i37]Rohitash Chandra, Royce Chen, Joshua A. Simmons:
Bayesian neural networks via MCMC: a Python-based tutorial. CoRR abs/2304.02595 (2023) - [i36]Azal Ahmad Khan, Omkar Chaudhari
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. CoRR abs/2304.02858 (2023) - [i35]Mahsa Tavakoli, Rohitash Chandra, Fengrui Tian, Cristián Bravo:
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams. CoRR abs/2304.10740 (2023) - [i34]Rohitash Chandra, Jayesh Sonawane, Janhavi Lande, Cathy Yu:
An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines. CoRR abs/2306.13797 (2023) - [i33]Chaarvi Bansal, Rohitash Chandra, Vinti Agarwal, P. R. Deepa:
A clustering and graph deep learning-based framework for COVID-19 drug repurposing. CoRR abs/2306.13995 (2023) - [i32]Honghui Wang, Weiming Zhi, Gustavo Batista, Rohitash Chandra:
Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning. CoRR abs/2309.09021 (2023) - 2022
- [j36]Rohitash Chandra
Semantic and Sentiment Analysis of Selected Bhagavad Gita Translations Using BERT-Based Language Framework. IEEE Access 10: 21291-21315 (2022) - [j35]Anuraganand Sharma
SMOTified-GAN for Class Imbalanced Pattern Classification Problems. IEEE Access 10: 30655-30665 (2022) - [j34]Rohitash Chandra
Revisiting Bayesian Autoencoders With MCMC. IEEE Access 10: 40482-40495 (2022) - [j33]Arpit Kapoor
Bayesian neuroevolution using distributed swarm optimization and tempered MCMC. Appl. Soft Comput. 129: 109528 (2022) - [j32]Amit Krishan Kumar
Deep learning for predicting respiratory rate from biosignals. Comput. Biol. Medicine 144: 105338 (2022) - [j31]Hardik A. Jain
CoviRx: A User-Friendly Interface for Systematic Down-Selection of Repurposed Drug Candidates for COVID-19. Data 7(11): 164 (2022) - [j30]Rohitash Chandra
Distributed Bayesian optimisation framework for deep neuroevolution. Neurocomputing 470: 51-65 (2022) - [j29]Giang Ngo, Rodney Beard
Evolutionary bagging for ensemble learning. Neurocomputing 510: 1-14 (2022) - [j28]Hodjat Shirmard
A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data. Remote. Sens. 14(4): 819 (2022) - [i31]Rohitash Chandra, Venkatesh Kulkarni:
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework. CoRR abs/2201.03115 (2022) - [i30]Rohitash Chandra, Yash Vardhan Sharma:
Surrogate-assisted distributed swarm optimisation for computationally expensive models. CoRR abs/2201.06843 (2022) - [i29]Shelvin Chand, Kousik Rajesh, Rohitash Chandra:
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem. CoRR abs/2204.11162 (2022) - [i28]Rohitash Chandra, Mukul Ranjan
Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita. CoRR abs/2205.11020 (2022) - [i27]Mingyue Kang, Seshadri Vasan, Laurence O. W. Wilson, Rohitash Chandra:
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19. CoRR abs/2208.01439 (2022) - [i26]Giang Ngo, Rodney Beard, Rohitash Chandra:
Evolutionary bagging for ensemble learning. CoRR abs/2208.02400 (2022) - 2021
- [j27]Rohitash Chandra
Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction. IEEE Access 9: 83105-83123 (2021) - [j26]Rohitash Chandra
Biden vs Trump: Modeling US General Elections Using BERT Language Model. IEEE Access 9: 128494-128505 (2021) - [j25]Rohitash Chandra
Bayesian Graph Convolutional Neural Networks via Tempered MCMC. IEEE Access 9: 130353-130365 (2021) - [j24]Rohitash Chandra, Sally Cripps, Nathaniel Butterworth, R. Dietmar Müller
Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning. Environ. Model. Softw. 139: 105002 (2021) - [i25]Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan:
Deep learning via LSTM models for COVID-19 infection forecasting in India. CoRR abs/2101.11881 (2021) - [i24]Animesh Tiwari, Rishabh Gupta, Rohitash Chandra:
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown. CoRR abs/2102.10551 (2021) - [i23]Hodjat Shirmard, Ehsan Farahbakhsh
A review of machine learning in processing remote sensing data for mineral exploration. CoRR abs/2103.07678 (2021) - [i22]Rohitash Chandra, Shaurya Goyal, Rishabh Gupta:
Evaluation of deep learning models for multi-step ahead time series prediction. CoRR abs/2103.14250 (2021) - [i21]Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky:
Revisiting Bayesian Autoencoders with MCMC. CoRR abs/2104.05915 (2021) - [i20]Animesh Renanse, Rohitash Chandra, Alok Sharma:
Memory Capacity of Neural Turing Machines with Matrix Representation. CoRR abs/2104.07454 (2021) - [i19]Rohitash Chandra, Ayush Bhagat, Manavendra Maharana, Pavel N. Krivitsky:
Bayesian graph convolutional neural networks via tempered MCMC. CoRR abs/2104.08438 (2021) - [i18]Rohitash Chandra, Aswin Krishna:
COVID-19 sentiment analysis via deep learning during the rise of novel cases. CoRR abs/2104.10662 (2021) - [i17]Anuraganand Sharma, Prabhat Kumar Singh, Rohitash Chandra:
SMOTified-GAN for class imbalanced pattern classification problems. CoRR abs/2108.03235 (2021) - 2020
- [j23]Rohitash Chandra
Surrogate-assisted parallel tempering for Bayesian neural learning. Eng. Appl. Artif. Intell. 94: 103700 (2020) - [j22]Jodie Pall, Rohitash Chandra
Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics. Environ. Model. Softw. 125: 104610 (2020) - [j21]Rohitash Chandra
Bayesian neural multi-source transfer learning. Neurocomputing 378: 54-64 (2020) - [j20]Hodjat Shirmard, Ehsan Farahbakhsh
Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data. Remote. Sens. 12(8): 1261 (2020) - [j19]Ehsan Farahbakhsh
3DWofE: An open-source software package for three-dimensional weights of evidence modeling. Softw. Impacts 6: 100039 (2020)
2010 – 2019
- 2019
- [j18]Rohitash Chandra
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands. Comput. Geosci. 131: 89-101 (2019) - [j17]Rohitash Chandra
Langevin-gradient parallel tempering for Bayesian neural learning. Neurocomputing 359: 315-326 (2019) - [c51]Ratneel Deo
Multi-step-ahead Cyclone Intensity Prediction with Bayesian Neural Networks. PRICAI (2) 2019: 282-295 - 2018
- [j16]Rohitash Chandra
Co-evolutionary multi-task learning for dynamic time series prediction. Appl. Soft Comput. 70: 576-589 (2018) - [j15]Rohitash Chandra
Coevolutionary multi-task learning for feature-based modular pattern classification. Neurocomputing 319: 164-175 (2018) - [j14]Rohitash Chandra
Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks. Neural Process. Lett. 47(3): 993-1009 (2018) - [c50]Rohitash Chandra
Multi-Task Modular Backpropagation For Dynamic Time Series Prediction. IJCNN 2018: 1-7 - [c49]Rohitash Chandra
Bayesian Multi-task Learning for Dynamic Time Series Prediction. IJCNN 2018: 1-8 - [c48]Gary Wong, Anuraganand Sharma, Rohitash Chandra
Information Collection Strategies In Memetic Cooperative Neuroevolution For Time Series Prediction. IJCNN 2018: 1-6 - [c47]Yanfei Zhang, Rohitash Chandra
Cyclone Track Prediction with Matrix Neural Networks. IJCNN 2018: 1-8 - [i16]Rohitash Chandra, Danial Azam, R. Dietmar Müller, Tristan Salles, Sally Cripps:
BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands. CoRR abs/1805.03696 (2018) - [i15]Rohitash Chandra, R. Dietmar Müller, Ratneel Deo, Nathaniel Butterworth, Tristan Salles, Sally Cripps:
Multi-core parallel tempering Bayeslands for basin and landscape evolution. CoRR abs/1806.10939 (2018) - [i14]Jodie Pall, Rohitash Chandra, Danial Azam, Tristan Salles, Jody M. Webster, Sally Cripps:
BayesReef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics. CoRR abs/1808.02763 (2018) - [i13]Ehsan Farahbakhsh
Computer vision-based framework for extracting geological lineaments from optical remote sensing data. CoRR abs/1810.02320 (2018) - [i12]Rohitash Chandra, Konark Jain, Ratneel Vikash Deo, Sally Cripps:
Langevin-gradient parallel tempering for Bayesian neural learning. CoRR abs/1811.04343 (2018) - [i11]Rohitash Chandra, Konark Jain, Arpit Kapoor:
Surrogate-assisted parallel tempering for Bayesian neural learning. CoRR abs/1811.08687 (2018) - [i10]Richard Scalzo, David Kohn, Hugo K. H. Olierook, Gregory Houseman, Rohitash Chandra, Mark A. Girolami, Sally Cripps:
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success. CoRR abs/1812.00318 (2018) - [i9]Rohitash Chandra, Danial Azam, Arpit Kapoor, R. Dietmar Müller:
Surrogate-assisted Bayesian inversion for landscape and basin evolution models. CoRR abs/1812.08655 (2018) - 2017
- [j13]Shonal Chaudhry, Rohitash Chandra
Face detection and recognition in an unconstrained environment for mobile visual assistive system. Appl. Soft Comput. 53: 168-180 (2017) - [j12]Rohitash Chandra
Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction. Neurocomputing 243: 21-34 (2017) - [c46]Rohitash Chandra
Multi-task Modular Backpropagation for Feature-Based Pattern Classification. ICONIP (6) 2017: 558-566 - [c45]Rohitash Chandra
Bayesian Neural Learning via Langevin Dynamics for Chaotic Time Series Prediction. ICONIP (5) 2017: 564-573 - [c44]Rohitash Chandra
Dynamic Cyclone Wind-Intensity Prediction Using Co-Evolutionary Multi-task Learning. ICONIP (5) 2017: 618-627 - [c43]Rohitash Chandra
Co-evolutionary Multi-task Learning for Modular Pattern Classification. ICONIP (6) 2017: 692-701 - [c42]Rohitash Chandra
Towards an Affective Computational Model for Machine Consciousness. ICONIP (5) 2017: 897-907 - [i8]Rohitash Chandra:
An affective computational model for machine consciousness. CoRR abs/1701.00349 (2017) - [i7]Rohitash Chandra:
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks. CoRR abs/1701.04518 (2017) - [i6]Rohitash Chandra, Yew-Soon Ong, Chi-Keong Goh:
Co-evolutionary multi-task learning for dynamic time series prediction. CoRR abs/1703.01887 (2017) - [i5]Ratneel Vikash Deo, Rohitash Chandra, Anuraganand Sharma:
Stacked transfer learning for tropical cyclone intensity prediction. CoRR abs/1708.06539 (2017) - 2016
- [j11]Rohitash Chandra
Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance. Appl. Soft Comput. 49: 462-473 (2016) - [j10]Luc Rolland, Rohitash Chandra
The forward kinematics of the 6-6 parallel manipulator using an evolutionary algorithm based on generalized generation gap with parent-centric crossover. Robotica 34(1): 1-22 (2016) - [c41]Ravneil Nand
Competitive Island Cooperative Neuro-evolution of Feedforward Networks for Time Series Prediction. ACALCI 2016: 160-170 - [c40]Ravneil Nand
Reverse Neuron Level Decomposition for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction. ACALCI 2016: 171-182 - [c39]Ravneil Nand
Coevolutionary Feature Selection and Reconstruction in Neuro-Evolution for Time Series Prediction. ACALCI 2016: 285-297 - [c38]Kavitesh Bali, Rohitash Chandra
Contribution based multi-island competitive cooperative coevolution. CEC 2016: 1823-1830 - [c37]Shamina Hussein
Multi-step-ahead chaotic time series prediction using coevolutionary recurrent neural networks. CEC 2016: 3084-3091 - [c36]Mashud Rana
Cooperative neuro-evolutionary recurrent neural networks for solar power prediction. CEC 2016: 4691-4698 - [c35]Rohitash Chandra
On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution. CEC 2016: 4823-4830 - [c34]Shamina Hussein
Chaotic Feature Selection and Reconstruction in Time Series Prediction. ICONIP (3) 2016: 3-11 - [c33]Rohitash Chandra
Evolutionary Multi-task Learning for Modular Training of Feedforward Neural Networks. ICONIP (2) 2016: 37-46 - [c32]Gary Wong, Rohitash Chandra
Memetic Cooperative Neuro-Evolution for Chaotic Time Series Prediction. ICONIP (3) 2016: 299-308 - [c31]Shonal Chaudhry, Rohitash Chandra
Unconstrained Face Detection from a Mobile Source Using Convolutional Neural Networks. ICONIP (2) 2016: 567-576 - [c30]Ratneel Deo
Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction. IJCNN 2016: 489-496 - [c29]Rohitash Chandra
An architecture for encoding two-dimensional cyclone track prediction problem in coevolutionary recurrent neural networks. IJCNN 2016: 4865-4872 - 2015
- [j9]Rohitash Chandra
Global-local population memetic algorithm for solving the forward kinematics of parallel manipulators. Connect. Sci. 27(1): 22-39 (2015) - [j8]Rohitash Chandra
Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction. IEEE Trans. Neural Networks Learn. Syst. 26(12): 3123-3136 (2015) - [c28]Kavitesh K. Bali, Rohitash Chandra
Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation. Australasian Conference on Artificial Intelligence 2015: 34-48 - [c27]Rohitash Chandra
Competitive two-island cooperative coevolution for real parameter global optimisation. CEC 2015: 93-100 - [c26]Rohitash Chandra
Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction. CEC 2015: 101-108 - [c25]Rohitash Chandra
Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region. CEC 2015: 1784-1791 - [c24]Rohitash Chandra
Coevolutionary Recurrent Neural Networks for Prediction of Rapid Intensification in Wind Intensity of Tropical Cyclones in the South Pacific Region. ICONIP (3) 2015: 43-52 - [c23]Ravneil Nand
Neuron-Synapse Level Problem Decomposition Method for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction. ICONIP (3) 2015: 90-100 - [c22]Kavitesh K. Bali, Rohitash Chandra
Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization. ICONIP (3) 2015: 127-136 - [c21]Kavitesh K. Bali, Rohitash Chandra
Competitive Island-Based Cooperative Coevolution for Efficient Optimization of Large-Scale Fully-Separable Continuous Functions. ICONIP (3) 2015: 137-147 - [c20]Gary Wong, Rohitash Chandra
Enhancing Competitive Island Cooperative Neuro-Evolution Through Backpropagation for Pattern Classification. ICONIP (1) 2015: 293-301 - [c19]Rohitash Chandra
Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region. IJCNN 2015: 1-8 - [c18]Rohitash Chandra
Competitive two-island cooperative co-evolution for training feedforward neural networks for pattern classification problems. IJCNN 2015: 1-8 - [i4]Shonal Chaudhry, Rohitash Chandra:
Design of a Mobile Face Recognition System for Visually Impaired Persons. CoRR abs/1502.00756 (2015) - [i3]Daryl Abel, Bulou Gavidi, Nicholas Rollings, Rohitash Chandra:
Development of an Android Application for an Electronic Medical Record System in an Outpatient Environment for Healthcare in Fiji. CoRR abs/1503.00810 (2015) - [i2]Emmenual Reddy, Sarnil Kumar, Nicholas Rollings, Rohitash Chandra:
Mobile Application for Dengue Fever Monitoring and Tracking via GPS: Case Study for Fiji. CoRR abs/1503.00814 (2015) - [i1]Swaran S. Ravindra
A Study of the Management of Electronic Medical Records in Fijian Hospitals. CoRR abs/1507.03659 (2015) - 2014
- [j7]Rohitash Chandra
Memetic cooperative coevolution of Elman recurrent neural networks. Soft Comput. 18(8): 1549-1559 (2014) - [c17]Shelvin Chand
Multi-objective cooperative coevolution of neural networks for time series prediction. IJCNN 2014: 190-197 - [c16]Shelvin Chand
Cooperative coevolution of feed forward neural networks for financial time series problem. IJCNN 2014: 202-209 - [c15]Rohitash Chandra
Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction. IJCNN 2014: 565-572 - 2013
- [c14]Rohitash Chandra
Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. IJCNN 2013: 1-8 - 2012
- [b1]Rohitash Chandra:
Problem Decomposition and Adaptation in Cooperative Neuro-Evolution. Victoria University of Wellington, New Zealand, 2012 - [j6]Rohitash Chandra
Crossover-based local search in cooperative co-evolutionary feedforward neural networks. Appl. Soft Comput. 12(9): 2924-2932 (2012) - [j5]Rohitash Chandra
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86: 116-123 (2012) - [j4]Rohitash Chandra
On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87: 33-40 (2012) - [j3]Rohitash Chandra
Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks. Soft Comput. 16(6): 1009-1020 (2012) - [c13]Rohitash Chandra
Application of Cooperative Convolution Optimization for 13C Metabolic Flux Analysis: Simulation of Isotopic Labeling Patterns Based on Tandem Mass Spectrometry Measurements. SEAL 2012: 178-187 - 2011
- [j2]Rohitash Chandra
On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics. Appl. Math. Comput. 217(22): 8997-9008 (2011) - [j1]Rohitash Chandra
Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing 74(17): 3223-3234 (2011) - [c12]Rohitash Chandra
A memetic framework for cooperative coevolution of recurrent neural networks. IJCNN 2011: 673-680 - [c11]Rohitash Chandra
Modularity adaptation in cooperative coevolution of feedforward neural networks. IJCNN 2011: 681-688 - 2010
- [c10]Rohitash Chandra
An Encoding Scheme for Cooperative Coevolutionary Feedforward Neural Networks. Australasian Conference on Artificial Intelligence 2010: 253-262
2000 – 2009
- 2009
- [c9]Rohitash Chandra
A meta-heuristic paradigm for solving the forward kinematics of 6-6 general parallel manipulator. CIRA 2009: 171-176 - [c8]Rohitash Chandra
Solving the forward kinematics of the 3RPR planar parallel manipulator using a hybrid meta-heuristic paradigm. CIRA 2009: 177-182 - [c7]Luc Rolland, Rohitash Chandra
Forward kinematics of the 3RPR planar parallel manipulators using real coded genetic algorithms. ISCIS 2009: 381-386 - 2008
- [c6]Rohitash Chandra, Christian W. Omlin:
Hybrid Evolutionary One-Step Gradient Descent for Training Recurrent Neural Networks. GEM 2008: 305-311 - 2007
- [c5]Rohitash Chandra, Christian W. Omlin:
A Hybrid Recurrent Neural Networks Architecture Inspired by Hidden Markov Models: Training and Extraction of Deterministic Finite Automaton. Artificial Intelligence and Pattern Recognition 2007: 278-285 - [c4]Rohitash Chandra, Christian W. Omlin:
The Comparison and Combination of Genetic and Gradient Descent Learning in Recurrent Neural Networks: An Application to Speech Phoneme Classification. Artificial Intelligence and Pattern Recognition 2007: 286-293 - [c3]Rohitash Chandra, Christian W. Omlin:
Knowledge Discovery using Artificial Neural Networks for a Conservation Biology Domain. DMIN 2007: 221-227 - [c2]Rohitash Chandra, Christian W. Omlin:
Hybrid Recurrent Neural Networks: An Application to Phoneme Classification. GEM 2007: 57-62 - 2006
- [c1]Rohitash Chandra, Christian W. Omlin:
Training and extraction of fuzzy finite state automata in recurrent neural networks. Computational Intelligence 2006: 274-279
Coauthor Index
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last updated on 2024-11-11 22:23 CET by the dblp team
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