All Outputs (2)

Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data. (2023)
Conference Proceeding
STEWART, C., FUNG, WAI KEUNG, FOUGH, N. and PRABHU, R. 2023. Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data. In Proceedings of the 21st IEEE (Institute of Electrical and Electronics Engineers) Interregional NEWCAS conference 2023 (NEWCAS 2023), 26-28 June 2023, Edinburgh, UK. Piscataway: IEEE [online], article 10198101. Available from: https://doi.org/10.1109/NEWCAS57931.2023.10198101

Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Se... Read More about Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data..

Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation. (2023)
Thesis
ADEGBOYE, M.A. 2023. Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2071535

Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysi... Read More about Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation..