All Outputs (7)

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments (2018)
Presentation / Conference Contribution
McGough, S., Forshaw, M., Brennan, J., Al Moubayed, N., & Bonner, S. (2018). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. In 2018 Ninth International Green and Sustainable Computing Conference (IGSC) (1-8). https://doi.org/10.1109/igcc.2018.8752115

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time - volunteer computing. This has great advantages... Read More about Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments.

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Presentation / Conference Contribution
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.

CAM: A Combined Attention Model for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018). CAM: A Combined Attention Model for Natural Language Inference. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, …J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (Big Data) ; proceedings (1009-1014). https://doi.org/10.1109/bigdata.2018.8622057

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl... Read More about CAM: A Combined Attention Model for Natural Language Inference.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018, December). An Exploration of Dropout with RNNs for Natural Language Inference. Presented at ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.

Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models (2018)
Presentation / Conference Contribution
Alhassan, Z., McGough, S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018, October). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. Presented at 27th International Conference on Artificial Neural Networks (ICANN)., Rhodes, Greece

Clinical data is usually observed and recorded at irregular intervals and includes: evaluations, treatments, vital sign and lab test results. These provide an invaluable source of information to help diagnose and understand medical conditions. In thi... Read More about Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models.

Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data (2018)
Presentation / Conference Contribution
Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings (541-546). https://doi.org/10.1109/icmla.2018.00087

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an inva... Read More about Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data.