| Applying neural networks: a practical guide K Swingler Morgan Kaufmann, 1996 | 1255 | 1996 |
| Presymptomatic prediction of sepsis in intensive care unit patients RA Lukaszewski, AM Yates, MC Jackson, K Swingler, JM Scherer, ... Clinical and Vaccine Immunology 15 (7), 1089-1094, 2008 | 93 | 2008 |
| The development of a side effect risk assessment tool (ASyMS©-SERAT) for use in patients with breast cancer undergoing adjuvant chemotherapy R Maguire, J Cowie, C Leadbetter, K McCall, K Swingler, L McCann, ... Journal of Research in Nursing 14 (1), 27-40, 2009 | 68 | 2009 |
| Financial prediction: Some pointers, pitfalls and common errors K Swingler Neural Computing & Applications 4 (4), 192-197, 1996 | 43 | 1996 |
| Consensus on items and quantities of clinical equipment required to deal with a mass casualties big bang incident: a national Delphi study EAS Duncan, K Colver, N Dougall, K Swingler, J Stephenson, ... BMC emergency medicine 14 (1), 5, 2014 | 15 | 2014 |
| Learning and searching pseudo-Boolean surrogate functions from small samples K Swingler Evolutionary Computation 28 (2), 317-338, 2020 | 10 | 2020 |
| A semi-supervised corpus annotation for saudi sentiment analysis using twitter A Alqarafi, A Adeel, A Hawalah, K Swingler, A Hussain International Conference on Brain Inspired Cognitive Systems, 589-596, 2018 | 9 | 2018 |
| On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems. K Swingler IJCCI, 152-157, 2012 | 9 | 2012 |
| Producing a neural network for monitoring driver alertness from steering actions K Swingler, LS Smith Neural Computing & Applications 4 (2), 96-104, 1996 | 9 | 1996 |
| SUMS: A flexible approach to the teaching and learning of statistics MV Swingler, P Bishop, KM Swingler Psychology Learning & Teaching 8 (1), 39-45, 2009 | 7 | 2009 |
| Training and making calculations with mixed order hyper-networks K Swingler, LS Smith Neurocomputing 141, 65-75, 2014 | 6 | 2014 |
| A comparison of learning rules for mixed order hyper networks K Swingler Computational Intelligence (IJCCI), 2015 7th International Joint Conference …, 2015 | 5 | 2015 |
| An analysis of the local optima storage capacity of hopfield network based fitness function models K Swingler, L Smith Transactions on Computational Collective Intelligence XVII, 248-271, 2014 | 5 | 2014 |
| ASYMS-SERAT: a side-effect risk assessment tool to predict chemotherapy related toxicity in patients with cancer receiving chemotherapy J Cowie, K Swingler, C Leadbetter, R Maguire, K McCall, N Kearney HEALTHINF-International Conference on Health Informatics 2008, 225-230, 2008 | 5 | 2008 |
| Mixed Order Hyper-Networks for Function Approximation and Optimisation K Swingler University of Stirling, 2016 | 4 | 2016 |
| Opening the black box: analysing MLP functionality using walsh functions K Swingler Computational Intelligence, 303-323, 2016 | 4 | 2016 |
| Structure discovery in mixed order hyper networks K Swingler Big Data Analytics 1 (1), 8, 2016 | 3 | 2016 |
| Local optima suppression search in mixed order hyper networks K Swingler Proc. UKCI 2015, 2015 | 3 | 2015 |
| A Walsh Analysis of Multilayer Perceptron Function. K Swingler IJCCI (NCTA), 5-14, 2014 | 3 | 2014 |
| Sequence categorisation using multiple recurrent layers to create many trajectories through network state space KM Swingler Proc. ICANN 2, 1025-1028, 1994 | 3 | 1994 |
