Maritime piracy situation modelling with dynamic Bayesian networks JJ Dabrowski, JP De Villiers Information fusion 23, 116-130, 2015 | 73 | 2015 |
Systemic banking crisis early warning systems using dynamic Bayesian networks JJ Dabrowski, C Beyers, JP de Villiers Expert systems with applications 62, 225-242, 2016 | 72 | 2016 |
Dissolved oxygen prediction in prawn ponds from a group of one step predictors A Rahman, J Dabrowski, J McCulloch Information Processing in Agriculture 7 (2), 307-317, 2020 | 48 | 2020 |
ForecastNet: a time-variant deep feed-forward neural network architecture for multi-step-ahead time-series forecasting JJ Dabrowski, YF Zhang, A Rahman Neural Information Processing: 27th International Conference, ICONIP 2020 …, 2020 | 47 | 2020 |
State space models for forecasting water quality variables: an application in aquaculture prawn farming JJ Dabrowski, A Rahman, A George, S Arnold, J McCulloch Proceedings of the 24th ACM SIGKDD international conference on Knowledge …, 2018 | 32 | 2018 |
Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds M Rana, A Rahman, J Dabrowski, S Arnold, J McCulloch, B Pais Biosystems Engineering 208, 164-175, 2021 | 22 | 2021 |
Prediction of dissolved oxygen from pH and water temperature in aquaculture prawn ponds JJ Dabrowski, A Rahman, A George Proceedings of the Australasian joint conference on artificial intelligence …, 2018 | 20 | 2018 |
A unified model for context-based behavioural modelling and classification JJ Dabrowski, JP De Villiers Expert Systems with Applications 42 (19), 6738-6757, 2015 | 19 | 2015 |
An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management A Rahman, M Xi, JJ Dabrowski, J McCulloch, S Arnold, M Rana, A George, ... Aquacultural Engineering 95, 102192, 2021 | 17 | 2021 |
Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion JJ Dabrowski, JP de Villiers, C Beyers Information Fusion 42, 75-101, 2018 | 17 | 2018 |
Enforcing mean reversion in state space models for prawn pond water quality forecasting JJ Dabrowski, A Rahman, DE Pagendam, A George Computers and electronics in agriculture 168, 105120, 2020 | 15 | 2020 |
Sequence-to-sequence imputation of missing sensor data JJ Dabrowski, A Rahman AI 2019: Advances in Artificial Intelligence: 32nd Australasian Joint …, 2019 | 13 | 2019 |
Identification of variables affecting production outcome in prawn ponds: A machine learning approach A Rahman, S Arnold, JJ Dabrowski Computers and electronics in agriculture 156, 618-626, 2019 | 12 | 2019 |
Bayesian physics informed neural networks for data assimilation and spatio-temporal modelling of wildfires JJ Dabrowski, DE Pagendam, J Hilton, C Sanderson, D MacKinlay, ... Spatial Statistics 55, 100746, 2023 | 9 | 2023 |
Context-based behaviour modelling and classification of marine vessels in an abalone poaching situation JJ Dabrowski, JP de Villiers, C Beyers Engineering Applications of Artificial Intelligence 64, 95-111, 2017 | 8 | 2017 |
A spatio-temporal neural network forecasting approach for emulation of firefront models A Bolt, C Huston, P Kuhnert, JJ Dabrowski, J Hilton, C Sanderson 2022 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2022 | 7 | 2022 |
Towards data assimilation in level-set wildfire models using Bayesian filtering JJ Dabrowski, C Huston, J Hilton, S Mangeon, P Kuhnert arXiv preprint arXiv:2206.08501, 2022 | 5 | 2022 |
A log-additive neural model for spatio-temporal prediction of groundwater levels D Pagendam, S Janardhanan, J Dabrowski, D MacKinlay Spatial Statistics 55, 100740, 2023 | 4 | 2023 |
An automatic quality evaluation procedure for third-party daily rainfall observations and its application over Australia M Li, Q Shao, JJ Dabrowski, A Rahman, A Powell, B Henderson, ... Stochastic Environmental Research and Risk Assessment 37 (7), 2473-2493, 2023 | 2 | 2023 |
An emulation framework for fire front spread A Bolt, JJ Dabrowski, C Huston, P Kuhnert arXiv preprint arXiv:2203.12160, 2022 | 2 | 2022 |