| Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms P Shine, MD Murphy, J Upton, T Scully Computers and electronics in agriculture 150, 74-87, 2018 | 80 | 2018 |
| Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms P Shine, T Scully, J Upton, MD Murphy Computers and electronics in agriculture 148, 337-346, 2018 | 58 | 2018 |
| Simplified method to derive the Kalman Filter covariance matrices to predict wind speeds from a NWP model C Lynch, MJ OMahony, T Scully Energy procedia 62, 676-685, 2014 | 52 | 2014 |
| Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis P Shine, T Scully, J Upton, L Shalloo, MD Murphy Applied Energy 210, 529-537, 2018 | 42 | 2018 |
| Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine P Shine, T Scully, J Upton, MD Murphy Applied energy 250, 1110-1119, 2019 | 41 | 2019 |
| Wireless LAN load-balancing with genetic algorithms T Scully, KN Brown International Conference on Innovative Techniques and Applications of …, 2008 | 37 | 2008 |
| Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources QA Phan, T Scully, M Breen, MD Murphy Energy conversion and management 174, 157-174, 2018 | 33 | 2018 |
| Coalition calculation in a dynamic agent environment T Scully, MG Madden, G Lyons Proceedings of the twenty-first international conference on Machine learning, 93, 2004 | 24 | 2004 |
| Genetic optimisation for a stochastic model for opportunistic maintenance planning of offshore wind farms K Kennedy, P Walsh, TW Mastaglio, T Scully 2016 4th International Symposium on Environmental Friendly Energies and …, 2016 | 16 | 2016 |
| On the importance of spatial perception for the design of adaptive user interfaces T Deuschel, T Scully 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing …, 2016 | 15 | 2016 |
| Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance P Joshi, C Thapa, S Camtepe, M Hasanuzzamana, T Scully, H Afli arXiv preprint arXiv:2109.09246, 2021 | 14 | 2021 |
| Facilitating high levels of wind penetration in a smart grid through the optimal utilization of battery storage in microgrids: An analysis of the trade-offs between economic … QA Phan, T Scully, M Breen, MD Murphy Energy conversion and management 206, 112354, 2020 | 14 | 2020 |
| Enabling all in-edge deep learning: a literature review P Joshi, M Hasanuzzaman, C Thapa, H Afli, T Scully IEEE Access 11, 3431-3460, 2023 | 13 | 2023 |
| An efficient platform for the automatic extraction of patterns in native code J Escalada, F Ortin, T Scully Scientific Programming 2017, 2017 | 13 | 2017 |
| Dual vote: A novel user interface for e-voting systems D MacNamara, F Carmody, T Scully, K Oakley, E Quane, JP Gibson Proceedings of the IADIS International Conference on Interfaces and Human …, 2010 | 13 | 2010 |
| DualVote: Addressing usability and verifiability issues in electronic voting systems D MacNamara, T Scully, JP Gibson, K Oakley, F Carmody, E Quane CeDEM'11: Conference for E-Democracy and Open Government, 313-322, 2011 | 12 | 2011 |
| One-day-ahead cost optimisation for a multi-energy source building using a genetic algorithm PQ An, MD Murphy, MC Breen, T Scully 2016 UKACC 11th International Conference on Control (CONTROL), 1-6, 2016 | 9 | 2016 |
| Economic Optimisation for a Building with an integrated Micro-grid connected to the National Grid PQ An, MD Murphy, MC Breen, T Scully 2015 World Congress on Sustainable Technologies (WCST), 140-144, 2015 | 9 | 2015 |
| Performance and information leakage in splitfed learning and multi-head split learning in healthcare data and beyond P Joshi, C Thapa, S Camtepe, M Hasanuzzaman, T Scully, H Afli Methods and Protocols 5 (4), 60, 2022 | 8 | 2022 |
| Improving type information inferred by decompilers with supervised machine learning J Escalada, T Scully, F Ortin arXiv preprint arXiv:2101.08116, 2021 | 7 | 2021 |
