All Outputs (12)

Deep learning for digitising complex engineering drawings. (2024)
Thesis
JAMIESON, L. 2024. Deep learning for digitising complex engineering drawings. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2795656

Vast amounts of documents are still commonly stored in undigitised formats. Consequently, the data they contain cannot be used to its full potential, as substantial manual effort is required to analyse it. Amongst these documents, engineering drawing... Read More about Deep learning for digitising complex engineering drawings..

Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.). Proceedings of the 2024 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, 3822Aachen: CEUR-WS [online], pages 11-18. Available from: https://ceur-ws.org/Vol-3822/short1.pdf

Evidence-based medicine (EBM) is a foundational element in medical research, playing a crucial role in shaping healthcare policies and clinical decision-making. However, the rigorous processes required for EBM, particularly during the abstract screen... Read More about Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models..

Few-shot symbol detection in engineering drawings. (2024)
Journal Article
JAMIESON, L., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Few-shot symbol detection in engineering drawings. Applied artificial intelligence [online], 38(1), article number e2406712. Available from: https://doi.org/10.1080/08839514.2024.2406712

Recently, there has been significant interest in digitizing engineering drawings due to their complexity and practical benefits. Symbol digitization, a critical aspect in this field, is challenging as utilizing Deep Learning-based methods to recogniz... Read More about Few-shot symbol detection in engineering drawings..

Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening. (2024)
Journal Article
OFORI-BOATENG, R., TRUJILLO-ESCOBAR, T.G., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening. Artificial intelligence in medicine [online], 157, article number 102989. Available from: https://doi.org/10.1016/j.artmed.2024.102989

Systematic Review (SR) are foundational to influencing policies and decision-making in healthcare and beyond. SRs thoroughly synthesise primary research on a specific topic while maintaining reproducibility and transparency. However, the rigorous nat... Read More about Enhancing systematic reviews: an in-depth analysis on the impact of active learning parameter combinations for biomedical abstract screening..

A multiclass imbalanced dataset classification of symbols from piping and instrumentation diagrams. (2024)
Presentation / Conference Contribution
JAMIESON, L., MORENO-GARCÍA, C.F. and ELYAN, E. 2024. A multiclass imbalanced dataset classification of symbols from piping and instrumentation diagrams. In Barney Smith, E.H., Liwicki, M. and Peng, L. (eds.) Proceedings of the 18th International conference on Document analysis and recognition 2024 (ICDAR 2024), 30 August - 4 September 2024, Athens, Greece. Lecture notes in computer science, 14804. Cham: Springer [online], part 1, pages 3-16. Available from: https://doi.org/10.1007/978-3-031-70533-5_1

Engineering diagrams provide rich source of information and are widely used across different industries. Recent years have seen growing research interest in developing solutions for processing and analysing these diagrams using wide range of image-pr... Read More about A multiclass imbalanced dataset classification of symbols from piping and instrumentation diagrams..

Towards automated remote inspection of anomalies in offshore components. (2024)
Thesis
TORAL QUIJAS, L.A. 2024. Towards automated remote inspection of anomalies in offshore components. Robert Gordon University, MRes thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801306

This dissertation marks a significant advancement in offshore structural inspections, focusing on the development, integration and evaluation of advanced deep-learning models. The research encompasses: a thorough literature review, identifying innova... Read More about Towards automated remote inspection of anomalies in offshore components..

Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRANTUGA, N. and MORENO-GARCIA, C.F. 2024. Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. In Finkelstein, J., Moskovitch, R. and Parimbelli, E. (eds.) Proceedings of the 22nd Artificial intelligence in medicine international conference 2024 (AIME 2024), 9-12 July 2024, Salt Lake City, UT, USA. Lecture notes in computer science, 14844. Cham: Springer [online], part I, pages 261-272. Available from: https://doi.org/10.1007/978-3-031-66538-7_26

Evidence-based medicine (EBM) represents a cornerstone in medical research, guiding policy and decision-making. However, the robust steps involved in EBM, particularly in the abstract screening stage, present significant challenges to researchers. Nu... Read More about Enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models..

Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection. (2024)
Journal Article
JAMIESON, L., MORENO-GARCIA, C.F. and ELYAN, E. 2025. Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection. International journal on document analysis and recognition [online], 28, pages 71-84. Available from: https://doi.org/10.1007/s10032-024-00492-9

Construction drawings are frequently stored in undigitised formats and consequently, their analysis requires substantial manual effort. This is true for many crucial tasks, including material takeoff where the purpose is to obtain a list of the equip... Read More about Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection..

Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review. (2024)
Journal Article
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review. Artificial intelligence review [online], 57(8), article number 200. Available from: https://doi.org/10.1007/s10462-024-10844-w

Systematic reviews (SRs) constitute a critical foundation for evidence-based decision-making and policy formulation across various disciplines, particularly in healthcare and beyond. However, the inherently rigorous and structured nature of the SR pr... Read More about Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review..

A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews. (2024)
Presentation / Conference Contribution
OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C. 2024. A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews. In Duh, K., Gomez, H. and Bethard, S. (eds.) Proceedings of the 2024 North American Chapter of the Association for Computational Linguistics conference (NAACL 2024): human language technologies, 16-21 June 2024, Mexico City, Mexico. Stroudsburg, PA: ACL [online], volume 1: long papers, pages 3725-3736. Available from: https://doi.org/10.18653/v1/2024.naacl-long.206

Systematic Reviews (SRs) are foundational in healthcare for synthesising evidence to inform clinical practices. Traditionally skewed towards English-language databases, SRs often exclude significant research in other languages, leading to potential b... Read More about A zero-shot monolingual dual stage information retrieval system for Spanish biomedical systematic literature reviews..

A review of deep learning methods for digitisation of complex documents and engineering diagrams. (2024)
Journal Article
JAMIESON, L., MORENO-GARCIA, C.F. and ELYAN, E. 2024. A review of deep learning methods for digitisation of complex documents and engineering diagrams. Artificial intelligence review [online], 57(6), article number 136. Available from: https://doi.org/10.1007/s10462-024-10779-2

This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines)... Read More about A review of deep learning methods for digitisation of complex documents and engineering diagrams..

Two-layer ensemble of deep learning models for medical image segmentation. (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], 16(3), pages 1141-1160. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation..