Welcome to OpenAIR@RGU

Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. (2024)
Journal Article
TOLIE, H.F., REN, J., CHEN, R., ZHAO, H. and ELYAN, E. 2025. Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. Engineering applications of artificial intelligence [online], 141, article number 109730. Available from: https://doi.org/10.1016/j.engappai.2024.109730

In subsea environments, sound navigation and ranging (SONAR) images are widely used for exploring and monitoring infrastructures due to their robustness and insensitivity to low-light conditions. However, their quality can degrade during acquisition... Read More about Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain..

Benchmarking a novel particle swarm optimization dynamic model versus HOMER in optimally sizing grid-integrated hybrid PV–hydrogen energy systems. (2024)
Journal Article
ATTEYA, A.I. and ALI, D. 2024. Benchmarking a novel particle swarm optimization dynamic model versus HOMER in optimally sizing grid-integrated hybrid PV–hydrogen energy systems. Eng [online], 5(4), pages 3239-3258. Available from: https://doi.org/10.3390/eng5040170

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H2 energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER softwa... Read More about Benchmarking a novel particle swarm optimization dynamic model versus HOMER in optimally sizing grid-integrated hybrid PV–hydrogen energy systems..

Comparative analysis of mechanical response in epoxy nanocomposites reinforced with MXene and other carbon-based nano-fillers: an experimental and numerical study. (2024)
Journal Article
SAHARUDIN, M.S., HASBI, S., AHMAD, E.Z., SAGAR, S., DAOUSH, W.M. and INAM, F. 2024. Comparative analysis of mechanical response in epoxy nanocomposites reinforced with MXene and other carbon-based nano-fillers: an experimental and numerical study. Journal of advanced research in micro and nano engineering [online], 26(1), pages 54-65. Available from: https://doi.org/10.37934/armne.26.1.5465

This research introduces a finite element model tailored explicitly to assess the mechanical characteristics inherent in MXene/polymer nano-composite. The primary focus revolves around elucidating the performance attributes through numerical simulati... Read More about Comparative analysis of mechanical response in epoxy nanocomposites reinforced with MXene and other carbon-based nano-fillers: an experimental and numerical study..

Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance. (2024)
Presentation / Conference Contribution
ARSHAD, U., CORSAR, D. and NKISI-ORJI, I. 2024. Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 56-61. Available from: https://ceur-ws.org/Vol-3822/short7.pdf

With the growing complexity and increased volumes, regulatory texts are fast becoming a significant challenge for organisations to remain compliant. Traditional ways of summarising legal texts need to be more accommodating of critical, domain-specifi... Read More about Integrating KGs and ontologies with RAG for personalised summarisation in regulatory compliance..

SCaLe-QA: Sri Lankan case law embeddings for legal QA. (2024)
Presentation / Conference Contribution
JAYAWARDENA, L., WIRATUNGA, N., ABEYRATNE, R., MARTIN, K., NKISI-ORJI, I. and WEERASINGHE, R. 2024. SCaLe-QU: Sri Lankan case law embeddings for legal QA. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online], pages 47-55. Available from: https://ceur-ws.org/Vol-3822/short6.pdf

SCaLe-QA is a foundational system developed for Sri Lankan Legal Question Answering (LQA) by leveraging domain-specific embeddings derived from Supreme Court cases. The system is tailored to capture the unique linguistic and structural characteristic... Read More about SCaLe-QA: Sri Lankan case law embeddings for legal QA..