Staff profile

In her role as an Associate Professor in Computer Science, Dr. Noura Al Moubayed has been heavily involved in advancing the field of machine learning (ML) and deep learning (DL), particularly within healthcare contexts. Her focus has been on developing innovative ML and DL solutions aimed at addressing critical challenges in patient care. She leads numerous research projects and a lab of over 15 researchers, working on developing cutting-edge machine learning and deep learning solutions. Over the last seven years of her academic career, she has secured funding for 21 projects from various organisations such as EPSRC, IUK, NIHR, ERDF, and UKRI, totalling over £6 million. Her research has attracted media coverage and has been featured on BBC, ITV, Time Magazine, Wired Magazine, and NewScientist. In 2019, she was recognised among the top 20 women in AI in the UK by RE•WORK. Dr Al Moubayed also serves as an Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence and N8 CIR Machine Learning team lead for Durham

Drawing upon her expertise and leadership as the Head of Applied ML and AI at  Evergreen Life, Dr. Al Moubayed has the practical experience necessary to effectively transition research findings into real-world deployment, ensuring tangible impacts on healthcare delivery and patient outcomes.

Dr. Al Moubayed has over 10 years of extensive research experience in explainable machine learning and natural language processing. She has also contributed significantly to research on AI gender and racial fairness and has dedicated significant efforts to creating explainable ML models tailored for predicting organ failure in chemotherapy patients, aiming to enhance patient well-being and overall quality of life. This marks a significant advancement in precision medicine and patient-centred care.This project is funded under the Biomedical Catalyst grant in collaboration with UCL, UCL Hospitals, and Evergreen Life Ltd.

Additionally, her research on predicting Accident & Emergency (A&E) admissions and readmissions using explainable machine learning has been recognised and endorsed by the National Institute for Health Research (NIHR) and formed part of a Department of Health and Social Care policy briefing on addressing winter pressures in the NHS and also won the best talk award at the Society for Acute Medicine International Conference 2023.

Industrial Collaborators

Furhat Rotobtics

Cievert Ltd

Cardon RMP

WordNerds Ltd

Geoteric Ltd

Geospatial Research Ltd

FOOTY.COM Ltd

• Machine Learning for Healthcare
• Natural Language Processing
• Bias and Fairness in Machine Learning
• Explainable Machine Learning
• Multimodal Machine Learning
• Anomaly Detection
• Social Robotics
• Brain Computer Interfaces
• Evolutionary Computation

• 2019: Named among the top 30 women in AI in the UK by RE-WORK:
• 2019: ACM-W Inspire Conference Chair:
• 2019: Invited Speaker @ Robert Gordon University, Aberdeen:
• 2019: Organiser of the Computational Neurosciences Special Session: at the 15th Conference on Computability in Europe (CiE 2019)
• 2019: Program Committee member: International Workshop on Social & Emotion AI for Industry
• 2019: Invited Speaker: A Celebration of the University’s Diverse Strengths in Research Symposium
• 2019: Debate Panel member: AI & Society: for better or for worse? 
  Panel of AI and social science experts to discuss the role of artificial intelligence in our society, organised by Durham University and NINE DT
• 2019: Roundtable Panel Member: Machine Learning and Digital Humanities. The event is supported by the Newcastle University Humanities Research Institute (NUHRI) and Animating Text Newcastle University (ATNU)
• 2019: Keynote Speaker at NGSchool: Summer School in Bioinformatics & NGS Data Analysis,
• 2019: Invited Speaker: Unconventional Computation and Natural Computation Conference 2019, Tokyo, Japan
• 2018: ITV news coverage for the pilot study at Fellside Primary School: Discussing how the robotic head 'Robbie', will be used to help children with autism in the future.
• 2018: Sponsorship Chair: 29th British Machine Vision Conference
• 2018: Invited Speaker and Panel Member: 3rd ACM-W UK Inspire Conference
• 2018: Technical Program Committee Chair: ACM Multi Media Conference
• 2018: BBC News coverage for the pilot study at Fellside Primary School: Discussing how the robotic head 'Robbie', will be used to help children with autism in the future.
• 2018: Invited Speaker: Technologies of Crime, Justice and Security Conference
• 2017: Invited Speaker: Re-Work Deep Learning Summit - London
• 2017: Area Chair: Women in Machine Learning Workshop (part of NIPS)
• 2016: Keynote Speaker: NVIDIA's GPU Programming and Machine Learning Workshop 'Deep Learning Applications powered by GPGPUs'
• 2000: Grants Reviewer for the European Commission:
• 2000: Grants Reviewer for EPSRC:

Chapter in book

• Al Moubayed, N., Petrovski, A., & McCall, J. Mutual Information for Performance Assessment of Multi Objective Optimisers: Preliminary Results. In Intelligent Data Engineering and Automated Learning – IDEAL 2013 (537-544). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_65
• Al Moubayed, N., Petrovski, A., & McCall, J. A Novel Smart Multi-Objective Particle Swarm Optimisation using Decomposition. In Parallel Problem Solving from Nature, PPSN XI (1-10). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_1
• Al Moubayed, N., Petrovski, A., & McCall, J. Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation. In Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_13
• Al Moubayed, N., Petrovski, A., & McCall, J. D 2 MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance. In Evolutionary Computation in Combinatorial Optimization (75-86). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_7
• Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35
• Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37
• 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
• Al Moubayed, N., Wall, D., & McGough, A. (2017). Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine. In T. Tryfonas (Ed.), Human aspects of information security, privacy and trust : 5th International Conference, HAS 2017, held as part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, proceedings (287-295). Springer Verlag. https://doi.org/10.1007/978-3-319-58460-7_19

Conference Paper

• Al Moubayed, N., & Awwad Shiekh Hasan, B. (2009, December). Temporal White-Box Testing Using Evolutionary and Search-base Algorithms. Paper presented at 9th Annual Workshop on Computational Intelligence, Colchester, UK
• Yucer, S., Abarghouei, A. A., Al Moubayed, N., & Breckon, T. P. (2024, June). Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan
• Shentu, J., & Al Moubayed, N. (2024, January). CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA
• Stirling, J., & Moubayed, N. A. (2023, June). Addressing Performance Inconsistency in Domain Generalization for Image Classification. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia
• Burton, J., Al Moubayed, N., & Enshaei, A. (2023, June). Natural Language Explanations for Machine Learning Classification Decisions. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia
• Xiao, C., Li, Y., Hudson, G. T., Lin, C., & Al Moubayed, N. (2023, December). Length is a Curse and a Blessing for Document-level Semantics. Presented at The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore
• Xiao, C., Long, Y., & Al Moubayed, N. (2023, July). On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning. Presented at Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada
• Yucer, S., Poyser, M., Al Moubayed, N., & Breckon, T. (2022, October). Does lossy image compression affect racial bias within face recognition?. Presented at International Joint Conference on Biometrics (IJCB 2022), Abu Dhabi, UAE
• Ampomah, I., Burton, J., Enshaei, A., & Al Moubayed, N. (2022, June). Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France
• Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2023, November). Enhanced Methods for Evolution in-Materio Processors. Presented at IEEE International Conference on Rebooting Computing (ICRC 2021), Virtual
• Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan
• Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy
• Watson, M., Awwad Shiekh Hasan, B., & Al Moubayed, N. (2022, January). Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI
• Hudson, G. T., & Al Moubayed, N. (2022, June). MuLD: The Multitask Long Document Benchmark. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France
• Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. (2022, January). Measuring Hidden Bias within Face Recognition via Racial Phenotypes. Presented at Proc. Winter Conference on Applications of Computer Vision, Waikoloa, HI
• Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy
• Zhang, X., Al Moubayed, N., & Shum, H. P. (2022, September). Towards Graph Representation Learning Based Surgical Workflow Anticipation. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece
• Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy
• Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021, July). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual
• Watson, M., & Al Moubayed, N. (2021, January). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. Presented at The 25th International Conference on Pattern Recognition (ICPR2020), Milan, Italy
• Excell, E., & Al Moubayed, N. (2021, August). Towards Equal Gender Representation in the Annotations of Toxic Language Detection. Presented at 3rd Workshop on Gender Bias in Natural Language Processing (GeBNLP2021), International Joint Conference on Natural Language Processing (INCNLP2021), Bangkok, Thailand
• Yucer, S., Akcay, S., Al Moubayed, N., & Breckon, T. (2020, June). Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation. Presented at Computer Vision and Pattern Recognition Workshops, Seattle, USA
• 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
• 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
• 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
• Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018, December). CAM: A Combined Attention Model for Natural Language Inference. Presented at IEEE International Conference on Big Data., Seattle, WA, USA
• Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018, July). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. Presented at 2018 IEEE World Congress on Computational Intelligence (WCCI 2018)., Rio de Janeiro, Brazil
• 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
• 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
• 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
• Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018, December). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. Presented at IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018)., Orlando, Fl, USA
• 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
• Al Moubayed, N., Hasan, B. A. S., & McGough, A. S. (2017, May). Enhanced detection of movement onset in EEG through deep oversampling. Presented at 30th International Joint Conference on Neural Networks (IJCNN 2017), Anchorage, Alaska, USA
• McGough, A. S., Al Moubayed, N., & M, F. (2017, April). Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. Presented at ENERGY-SIM 2017, L'Aqua
• Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
• Al Moubayed, N., Vazquez-Alvarez, Y., McKay, A., & Vinciarelli, A. (2014, November). Face-Based Automatic Personality Perception. Presented at 22nd ACM international conference on Multimedia - MM '14, Orlando, Florida, USA
• Al Moubayed, N., Awwad Shiekh Hasan, B., Gan, J., Petrovski, A., & McCall, J. (2012, June). Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces. Presented at 2012 IEEE Congress on Evolutionary Computation, Brisbane, Australia
• Al Moubayed, N., Petrovski, A., & McCall, J. (2011, July). Clustering based leaders' selection in multi-objective evolutionary algorithms. Presented at Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11, Dublin, Irland
• Al Moubayed, N., Petrovski, A., & McCall, J. (2011, April). Multi-objective Optimisation of Cancer Chemotherapy using Smart PSO with Decomposition. Presented at 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), Paris, France
• Al Moubayed, N., Awwad Shiekh Hasan, B., Gan, J., Petrovski, A., & McCall, J. (2010, September). Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces. Presented at 2010 UK Workshop on Computational Intelligence (UKCI), Colchester, UK
• Al Moubayed, N., & Windisch, A. (2009, April). Temporal White-Box Testing Using Evolutionary Algorithms. Presented at 2009 International Conference on Software Testing, Verification, and Validation Workshops, Denver, CO
• Windisch, A., & Al Moubayed, N. (2009, April). Signal Generation for Search-Based Testing of Continuous Systems. Presented at 2009 International Conference on Software Testing, Verification, and Validation Workshops, Denver, CO

Doctoral Thesis

• Al Moubayed, N. (2014). Multi-objective particle swarm optimisation: methods and applications. (Thesis). Robert Gordon University. https://durham-repository.worktribe.com/output/1618029

Journal Article

• Yucer, S., Tekras, F., Al Moubayed, N., & Breckon, T. P. (in press). Racial Bias within Face Recognition: A Survey. ACM Computing Surveys,
• Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430
• Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in Veterinary Science, 11, Article 1352726. https://doi.org/10.3389/fvets.2024.1352726
• Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1
• Davies, H., Nenadic, G., Alfattni, G., Arguello Casteleiro, M., Al Moubayed, N., Farrell, S. O., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words. Frontiers in Veterinary Science, 11, Article 1352239. https://doi.org/10.3389/fvets.2024.1352239
• Chambers, P., Watson, M., Bridgewater, J., Forster, M. D., Roylance, R., Burgoyne, R., Masento, S., Steventon, L., Harmsworth King, J., Duncan, N., & al Moubayed, N. (2023). Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine, 12(17), 17856-17865. https://doi.org/10.1002/cam4.6418
• Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, 61, https://doi.org/10.1109/tgrs.2023.3273329
• Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001
• Boulitsakis Logothetis, S., Green, D., Holland, M., & Al Moubayed, N. (2023). Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Scientific Reports, 13(1), Article 13563. https://doi.org/10.1038/s41598-023-40661-0
• Farrell, S., Appleton, C., Noble, P.-J. M., & Al Moubayed, N. (2023). PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records. Scientific Reports, 13(1), Article 18015. https://doi.org/10.1038/s41598-023-45155-7
• Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309
• Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6
• Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science, 8(e974), Article e974. https://doi.org/10.7717/peerj-cs.974
• Winterbottom, T., Leone, A., & Al Moubayed, N. (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports, 12(1), Article 13468. https://doi.org/10.1038/s41598-022-15965-2
• Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing, 60, https://doi.org/10.1109/tgrs.2022.3169455
• Zuo, Z., Li, J., Xu, H., & Al Moubayed, N. (2021). Curvature-based feature selection with application in classifying electronic health records. Technological Forecasting and Social Change, 173, Article 121127. https://doi.org/10.1016/j.techfore.2021.121127
• 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
• Zuo, Z., Watson, M., Budgen, D., Hall, R., Kennelly, C., & Al Moubayed, N. (2021). Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study. JMIR Medical Informatics, 9(10), https://doi.org/10.2196/29871
• Hudson, G. T., & Al Moubayed, N. (2021). Ask me in your own words: paraphrasing for multitask question answering. PeerJ Computer Science, 7, Article e759. https://doi.org/10.7717/peerj-cs.759
• 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
• 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
• Al Moubayed, N., Petrovski, A., & McCall, J. (2014). D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces. Evolutionary Computation, 22(1), 47-77. https://doi.org/10.1162/evco_a_00104

Working Paper

• Yucer, S., Atapour-Abarghouei, A., Al Moubayed, N., & Breckon, T. P. Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics