Skip to main content

Research Repository

Advanced Search

Browse


AI and the complexity of Black photographic representation and critical analysis (2024)
Book Chapter
Sobers, S., & Edwards, Y. (in press). AI and the complexity of Black photographic representation and critical analysis. In AI and Photography. Bristol: Royal Photographic Society

In this chapter artist Yuko Edwards and academic Shawn Sobers discuss the complex dynamics of AI image making, with regards both the problems and opportunities in the cultural politics of Black and African heritage representation. The significant cha... Read More about AI and the complexity of Black photographic representation and critical analysis.

Estimating water storage from images (2024)
Conference Proceeding
Shahbaz, A., Yunas, S., Smith, L., & Staddon, C. (2024). Estimating water storage from images. In 2023 IEEE International Conference on Big Data (BigData) (3375-3379). https://doi.org/10.1109/BigData59044.2023.10386262

This paper introduces a novel approach to estimate domestic water storage within households by leveraging the classical computer vision technique of object detection. Ensuring universal access to safe drinking water is a critical component of achievi... Read More about Estimating water storage from images.

Artificial intelligence for occupational health and safety management in construction (2023)
Book Chapter
Perera, S., Paton-Cole, V., Gao, S., Francis, V., Urhal, P., Manu, P., …Babalola, A. (2023). Artificial intelligence for occupational health and safety management in construction. In P. Manu, S. Gao, P. J. S. Bartolo, V. Francis, & A. Sawhney (Eds.), Handbook of Construction Safety, Health and Well-being in the Industry 4.0 Era (154-168). London: Taylor & Francis (Routledge). https://doi.org/10.1201/9781003213796-15

Reducing occupational safety and health (OSH) incidents has been an area of significant importance to the construction industry. The industry remains one of the most dangerous, with significant occupational fatalities and injuries. Artificial intelli... Read More about Artificial intelligence for occupational health and safety management in construction.

Scope and arbitration in machine learning clinical EEG classification (2023)
Conference Proceeding
Zhu, Y., Canham, L., & Western, D. (2023). Scope and arbitration in machine learning clinical EEG classification. In 2023 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/SPMB59478.2023.10372635

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit... Read More about Scope and arbitration in machine learning clinical EEG classification.

Machine learning models in trusted research environments - Understanding operational risks (2023)
Journal Article
Ritchie, F., Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., …Smith, J. (2023). Machine learning models in trusted research environments - Understanding operational risks. International Journal of Population Data Science, 8(1), Article 2165. https://doi.org/10.23889/ijpds.v8i1.2165

IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amou... Read More about Machine learning models in trusted research environments - Understanding operational risks.

Vision detection for early signs of DD lesions and lameness within dairy cattle (2023)
Conference Proceeding
Shahbaz, A., Zhang, W., & Smith, M. (in press). Vision detection for early signs of DD lesions and lameness within dairy cattle.

Digital dermatitis stands as a primary cause of lameness in dairy cows, significantly impacting various facets of productivity. This paper proposes a two-stage vision system aimed at early detection of digital dermatitis (DD) lesions, ultimately prev... Read More about Vision detection for early signs of DD lesions and lameness within dairy cattle.

Regulating armed drone swarms under international law (2023)
Presentation / Conference
Pollard, M. (2023, November). Regulating armed drone swarms under international law. Paper presented at New Technologies and International Law, Charles University, Prague, Czech Republic

One of the most controversial military systems currently under development today are autonomous weapons systems (AWS). Indeed, many states are urging the United Nations to prohibit AWS by way of a specific treaty. The term AWS is, nevertheless, somew... Read More about Regulating armed drone swarms under international law.

Predicting social trust from implicit feedback (2023)
Conference Proceeding
Oshodin, E. (2023). Predicting social trust from implicit feedback. In Artificial Intelligence XL (228-233). https://doi.org/10.1007/978-3-031-47994-6_20

Element of trust exists in every network structure of diverse fields, but suitable computational methods for evaluating the trust remain a problem since there are different definitions of trust in diverse fields where several entities interact with e... Read More about Predicting social trust from implicit feedback.

Multiple innovations in characterizing piezoelectric materials (2023)
Presentation / Conference
Fotouhi, S., Athanasiadis, I., Shvarts, A., Kaczmarczyk, L., Liu, B., Pearce, C., & Cochran, S. (2023, November). Multiple innovations in characterizing piezoelectric materials. Poster presented at Electroceramics for End Users XII (ECEUXII), Glasgow

Characterisation of piezoelectric materials is a key part. The IEEE standard is the only approach that can produce a full elastopiezodielectric matrix. However, it requires multiple samples. This often involves difficulties preparing the samples for... Read More about Multiple innovations in characterizing piezoelectric materials.

Non-contact characterisation of piezoelectric materials (2023)
Presentation / Conference
Alexandrou, A., Fotouhi, S., Nelson, J., & Cochran, S. (2023, November). Non-contact characterisation of piezoelectric materials. Poster presented at Electroceramics for End Users XII (ECEUXII), Glagow, UK

Piezoelectric materials play a key role in various technological applications owing to their unique elastic-piezoelectric-dielectric (EPD) properties, often represented in matrix form. Traditional methods for obtaining these properties, as defined by... Read More about Non-contact characterisation of piezoelectric materials.

Multi-agent learning of asset maintenance plans through localised subnetworks (2023)
Journal Article
Pérez Hernández, M., Puchkova, A., & Parlikad, A. K. (2024). Multi-agent learning of asset maintenance plans through localised subnetworks. Engineering Applications of Artificial Intelligence, 127(Part B), Article 107362. https://doi.org/10.1016/j.engappai.2023.107362

Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems... Read More about Multi-agent learning of asset maintenance plans through localised subnetworks.

Achieving goals using reward shaping and curriculum learning (2023)
Presentation / Conference
Studley, M., hansen, M., anca, M., thomas, J., & pedamonti, D. (2023, November). Achieving goals using reward shaping and curriculum learning. Paper presented at Future Technologies Conference, San Francisco

Real-time control for robotics is a popular research area in the reinforcement learning community. Through the use of techniques such as reward shaping, researchers have managed to train online agents across a multitude of domains. Despite these adva... Read More about Achieving goals using reward shaping and curriculum learning.

An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure (2023)
Thesis
Gbadamosi, A. An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/10928540

The railway industry plays a pivotal role in the socioeconomic landscape of many countries. However, its operation poses considerable challenges in terms of safety, environmental impact, and the intricacies of intertwined technical and social structu... Read More about An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure.

Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic (2023)
Journal Article
Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (in press). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2263102

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented... Read More about Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic.

SACRO: Semi-Automated Checking Of Research Outputs (2023)
Presentation / Conference
Smith, J., Preen, R., Albashir, M., Ritchie, F., Green, E., Davy, S., …Bacon, S. (2023, September). SACRO: Semi-Automated Checking Of Research Outputs. Paper presented at UNECE Expert meeting on Statistical Data Confidentiality, Wiesbaden, Germany

Output checking can require significant resources, acting as a barrier to scaling up the research use of confidential data. We report on a project, SACRO, that is developing a general-purpose, semi-automatic output checking systems that works across... Read More about SACRO: Semi-Automated Checking Of Research Outputs.

A method to enable automatic extraction of cost and quantity data from hierarchical construction information documents to enable rapid digital comparison and analysis (2023)
Journal Article
Adanza Dopazo, D., Mahdjoubi, L., & Gething, B. (2023). A method to enable automatic extraction of cost and quantity data from hierarchical construction information documents to enable rapid digital comparison and analysis. Buildings, 13(9), Article 2286. https://doi.org/10.3390/buildings13092286

Context: Despite the effort put into developing standards for structuring construction costs and the strong interest in the field, most construction companies still perform the process of data gathering and processing manually. This provokes inconsis... Read More about A method to enable automatic extraction of cost and quantity data from hierarchical construction information documents to enable rapid digital comparison and analysis.

Child safeguarding and immersive technologies - An outline of the risks (2023)
Report
McIntosh, V. (2023). Child safeguarding and immersive technologies - An outline of the risks. London: NSPCC

Given the rapid growth of new technologies, including immersive environments, the current generation of extended reality products – Virtual Reality (VR) and Augmented Reality (AR) – and the clear shift towards the development of the metaverse, resear... Read More about Child safeguarding and immersive technologies - An outline of the risks.

Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics (2023)
Conference Proceeding
Phung, K., Ogunshile, E., & Aydin, M. E. (in press). Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics.

In the context of software quality assurance, Software Fault Prediction (SFP) serves as a critical technique to optimise costs and efforts by classifying software modules as faulty or not, using pertinent project characteristics. Despite considerable... Read More about Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics.

Deep learning-based multi-target regression for traffic-related air pollution forecasting (2023)
Journal Article
Akinosho, T. D., Bilal, M., Hayes, E. T., Ajayi, A., Ahmed, A., & Khan, Z. (2023). Deep learning-based multi-target regression for traffic-related air pollution forecasting. Machine Learning with Applications, 12, Article 100474. https://doi.org/10.1016/j.mlwa.2023.100474

Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air qua... Read More about Deep learning-based multi-target regression for traffic-related air pollution forecasting.