Skip to main content

Research Repository

Advanced Search

Outputs (83)

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (2021)
Journal Article
Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive mode... Read More about Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records.

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models (2020)
Book Chapter
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020 (633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode... Read More about Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.

On Modality Bias in the TVQA Dataset (2020)
Presentation / Conference Contribution
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2020, September). On Modality Bias in the TVQA Dataset. Presented at The British Machine Vision Conference (BMVC), Manchester, England

TVQA is a large scale video question answering (video-QA) dataset based on popular TV shows. The questions were specifically designed to require “both vision and language understanding to answer”. In this work, we demonstrate an inherent bias in the... Read More about On Modality Bias in the TVQA Dataset.

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm (2020)
Journal Article
Alhassan, Z., Budgen, D., Alshammari, R., & Moubayed, N. A. (2020). Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm. Journal of Medical Internet Research, 8(7), Article e18963. https://doi.org/10.2196/18963

Background: Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation... Read More about Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm.

Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling (2020)
Journal Article
Al Moubayed, N., McGough, S., & Awwad Shiekh Hasan, B. (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science, 6, Article e252. https://doi.org/10.7717/peerj-cs.252

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly... Read More about Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, May). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. Presented at 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction (2019)
Presentation / Conference Contribution
Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019, September). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. Presented at 28th International Conference on Artificial Neural Networks (ICANN2019), Munich, Germany

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Ty... Read More about Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.

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, October). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. Presented at 9th International Green and Sustainable Computing Conference., Pittsburgh, PA, US

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, October). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan

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.