All Outputs (94)

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..

Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems. (2024)
Journal Article
DANG, T., NGUYEN, T.T., LIEW, A.W.-C., ELYAN, E. and MCCALL, J. 2024. Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems. Knowledge-based systems [online], 304, article number 112522. Available from: https://doi.org/10.1016/j.knosys.2024.112522

Ensemble learning is a powerful machine learning strategy that combines multiple models e.g. classifiers to improve predictions beyond what any single model can achieve. Until recently, traditional ensemble methods typically use only one layer of mod... Read More about Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems..

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 - 04 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 fully automated processing and analysis of construction diagrams: AI-powered symbol detection. (2024)
Journal Article
JAMIESON, L., MORENO-GARCIA, C.F. and ELYAN, E. [2024]. Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection. International journal on document analysis and recognition [online], Latest Articles. 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..

A multimodel-based screening framework for C-19 using deep learning-inspired data fusion. (2024)
Journal Article
SHANKAR, A., RIZWAN, P., MEKALA, M.S., ELYAN, E., GANDOMI, A.H., MAPLE, C. and RODRIGUES, J.J.P.C. 2024. A multimodel-based screening framework for C-19 using deep learning-inspired data fusion. IEEE journal of biomedical and health informatics [online], Early Access. Available from: https://doi.org/10.1109/JBHI.2024.3400878

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field... Read More about A multimodel-based screening framework for C-19 using deep learning-inspired data fusion..

Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review. (2024)
Journal Article
HERNANDEZ MANZO, D.S., JIANG, Y., ELYAN, E. and ISAACS, J. [2024]. Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review. International journal of human-computer interaction [online], Latest Articles. Available from: https://doi.org/10.1080/10447318.2024.2352920

In the past five years, the textile industry has undergone significant transformations in response to evolving fashion trends and increased consumer garment turnover. To address the environmental impacts of fast fashion, the industry is embracing art... Read More about Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review..

Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. (2024)
Presentation / Conference Contribution
HASAN, M.J., ELYAN, E., YAN, Y., REN, J. and SARKER, M.M.K. 2024. Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th International conference on Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 220-228. Available from: https://doi.org/10.1007/978-981-97-1417-9_21

Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to identify the heat los... Read More about Segmentation framework for heat loss identification in thermal images: empowering Scottish retrofitting and thermographic survey companies..

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..

DICAM: deep inception and channel-wise attention modules for underwater image enhancement. (2024)
Journal Article
FARHADI TOLIE, H., REN, J. and ELYAN, E. 2024. DICAM: deep inception and channel-wise attention modules for underwater image enhancement. Neurocomputing [online], 584, article number 127585. Available from: https://doi.org/10.1016/j.neucom.2024.127585

In underwater environments, imaging devices suffer from water turbidity, attenuation of lights, scattering, and particles, leading to low quality, poor contrast, and biased color images. This has led to great challenges for underwater condition monit... Read More about DICAM: deep inception and channel-wise attention modules for underwater image enhancement..

Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. (2024)
Journal Article
HAYNES, S.C., JOHNSTON, P. and ELYAN, E. 2024. Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. Multimedia tools and applications [online], 83(31), pages 76753-76772. Available from: https://doi.org/10.1007/s11042-024-18543-y

The generalisability of deep neural network classifiers is emerging as one of the most important challenges of our time. The recent COVID-19 pandemic led to a surge of deep learning publications that proposed novel models for the detection of COVID-1... Read More about Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers..

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..

Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis and prevention. (2023)
Journal Article
ALI, H., SHAH, Z., ALAM, T., WIJAYATUNGA, P. and ELYAN, E. 2023. Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis and prevention. Frontiers in radiology [online], 3, article number 1349830. Available from: https://doi.org/10.3389/fradi.2023.1349830

Artificial Intelligence (AI) has gained huge attention in computer-aided decision-making in the healthcare domain. Many novel AI methods have been developed for disease diagnosis and prognosis which may support in the prevention of disease. Most dise... Read More about Recent advances in multimodal artificial intelligence for disease diagnosis, prognosis and prevention..

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin. (2023)
Journal Article
NUMAIR, M., ABOUSHADY, A.A., ARRAÑO-VARGAS, F., FARRAG, M.E. and ELYAN, E. 2023. Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin. Energies [online], 16(23), 7850. Available from: https://doi.org/10.3390/en16237850

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (𝜇 PMU), which is potentially impractical for the large number of nodes in LVDNs. Thi... Read More about Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin..

Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems. (2023)
Presentation / Conference Contribution
MEKALA, M.S., EYAD, E. and SRIVASTAVA, G. 2023. Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems. In Proceedings of the 10th IEEE (Institute of Electrical and Electronics Engineers) Data science and advanced analytics international conference 2023 (DSAA 2023), 9-13 October 2023, Thessaloniki, Greece. Piscataway: IEEE [online], 10302554. Available from: https://doi.org/10.1109/DSAA60987.2023.10302554

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring meas... Read More about Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems..

Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection. (2023)
Thesis
ABDULLAKUTTY, F.C. 2023. Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2270640

Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, att... Read More about Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection..

Robust cardiac segmentation corrected with heuristics. (2023)
Journal Article
CERVANTES-GUZMÁN, A., MCPHERSON, K., OLVERES, J., MORENO-GARCÍA, C.F., ROBLES, F.T., ELYAN, E. and ESCALANTE-RAMÍREZ, B. 2023. Robust cardiac segmentation corrected with heuristics. PLoS ONE [online], 18(10), article e0293560. https://doi.org/10.1371/journal.pone.0293560

Cardiovascular diseases related to the right side of the heart, such as Pulmonary Hypertension, are some of the leading causes of death among the Mexican (and worldwide) population. To avoid invasive techniques such as catheterizing the heart, improv... Read More about Robust cardiac segmentation corrected with heuristics..

Unmasking the imposters: task-specific feature learning for face presentation attack detection. (2023)
Presentation / Conference Contribution
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2023. Unmasking the imposters: task-specific feature learning for face presentation attack detection. In Proceedings of the 2023 International joint conference on neural networks (IJCNN2023), 18-23 June 2023, Gold Coast, Australia. Piscataway: IEEE [online], 10191953. Available from: https://doi.org/10.1109/IJCNN54540.2023.10191953

Presentation attacks pose a threat to the reliability of face recognition systems. A photograph, a video, or a mask representing an authorised user can be used to circumvent the face recognition system. Recent research has demonstrated high accuracy... Read More about Unmasking the imposters: task-specific feature learning for face presentation attack detection..

Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. (2023)
Presentation / Conference Contribution
TORAL-QUIJAS, L.A., ELYAN, E., MORENO-GARCÍA, C.F. and STANDER, J. 2023. Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. In Iliadis, L, Maglogiannis, I., Alonso, S., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 24th International conference on engineering applications of neural networks (EAAAI/EANN 2023), 14-17 June 2023, León, Spain. Communications in computer and information science, 1826. Cham: Springer [online], pages 217-226. Available from: https://doi.org/10.1007/978-3-031-34204-2_19

Inspecting circumferential welds in caissons is a critical task for ensuring the safety and reliability of structures in the offshore industry. However, identifying and classifying different types of circumferential welds can be challenging in subsea... Read More about Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections..

Towards handling temporal dependence in concept drift streams. (2023)
Thesis
WARES, S.B. 2023. Towards handling temporal dependence in concept drift streams. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2271523

Modern technological advancements have led to the production of an incomprehensible amount of data from a wide array of devices. A constant supply of new data provides an invaluable opportunity for access to qualitative and quantitative insights. Org... Read More about Towards handling temporal dependence in concept drift streams..

On the UK smart metering system and value of data for distribution system operators. (2023)
Presentation / Conference Contribution
NUMAIR, M., ABOUSHADY, A.A., FARRAG, M.E. and ELYAN, E. 2023. On the UK smart metering system and value of data for distribution system operators. In Proceedings of the 19th International conference on AC and DC power transmission 2023 (ACDC 2023), 1-3 March 2023, Glasgow, UK. IET conference proceedings, 2023(1). Stevenage: IET [online], pages 174-180. Available from: https://doi.org/10.1049/icp.2023.1326

The Smart Metering Implementation Programme (SMIP) is an ongoing energy infrastructure upgrade that is delivering 53 million smart electricity and gas meters for homes and small businesses in the UK. The programme is expected to deliver economic bene... Read More about On the UK smart metering system and value of data for distribution system operators..