All Outputs (6)

Large-scale Delivery of Mathematics to Mixed-ability, Multidisciplinary Scientists in a Lockdown (2022)
Journal Article
Wootton. (2022). Large-scale Delivery of Mathematics to Mixed-ability, Multidisciplinary Scientists in a Lockdown

Foundations of Numerical and Quantitative Methods for Scientists/Health are two modules that, between them, deliver foundational Mathematics to over 200 students in the Science and Health Foundation Years. With the onset of the Covid-19 lockdown, a t... Read More about Large-scale Delivery of Mathematics to Mixed-ability, Multidisciplinary Scientists in a Lockdown.

Authentic Assessment: A Foundation Year Case Study (2022)
Journal Article
Wootton. (2022). Authentic Assessment: A Foundation Year Case Study

The last few years have seen many universities increasingly move towards authentic assessment as a means of developing a fair, inclusive and relevant curriculum. Taking its cue from Mueller (2005), who defined authentic assessment as ‘a form of asses... Read More about Authentic Assessment: A Foundation Year Case Study.

Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks (2022)
Journal Article
Wootton, A. J., Day, C., & Haycock, P. W. (2022). Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks. Structural Health Monitoring, 21(6), 2910-2921. https://doi.org/10.1177/14759217221080718

The degradation of roads is an expensive problem: in the UK alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with... Read More about Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks.

Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer (2017)
Conference Proceeding
Butcher, J. B., Rutter, A. V., Wootton, A. J., Day, C. R., & Sulé-Suso, J. (2017). Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer. In Advances in Computational Intelligence Systems (183-190). https://doi.org/10.1007/978-3-319-66939-7_15

Lung cancer is a widespread disease and it is well understood that systematic, non-invasive and early detection of this progressive and life-threatening disorder is of vital importance for patient outcomes. In this work we present a convergence of fa... Read More about Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer.

Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX (2017)
Journal Article
Wootton, A. J., Butcher, J. B., Kyriacou, T., Day, C. R., & Haycock, P. W. (2017). Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX. Engineering Applications of Artificial Intelligence, 64, 152-163. https://doi.org/10.1016/j.engappai.2017.05.014

Echo State Networks (ESNs) and a Nonlinear Auto-Regressive Moving Average model with eXogenous inputs (NARMAX) have been applied to multi-sensor time-series data arising from a test footbridge which has been subjected to multiple potentially damaging... Read More about Structural Health Monitoring of a Footbridge using Echo State Networks and NARMAX.

Optimizing Echo State Networks for Static Pattern Recognition (2017)
Journal Article
Wooton, A. J., Taylor, S. L., Day, C., & Haycock, P. W. (2017). Optimizing Echo State Networks for Static Pattern Recognition. Cognitive Computation, 391-399. https://doi.org/10.1007/s12559-017-9468-2

Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually, in thi... Read More about Optimizing Echo State Networks for Static Pattern Recognition.