All Outputs (44)

Event classification on subsea pipeline inspection data using an ensemble of deep learning classifiers. (2024)
Journal Article
DANG, T., NGUYEN, T.T., LIEW, A.W.-C. and ELYAN, E. 2025. Event classification on subsea pipeline inspection data using an ensemble of deep learning classifiers. Cognitive computation [online], 17(1), article 10. Available from: https://doi.org/10.1007/s12559-024-10377-y

Subsea pipelines are the backbone of the modern oil and gas industry, transporting a total of 28% of global oil production. Due to several factors, such as corrosion or deformations, the pipelines might degrade over time, which might lead to serious... Read More about Event classification on subsea pipeline inspection data using an ensemble of deep learning classifiers..

A review of well life cycle integrity challenges in the oil and gas industry and its implications for sustained casing pressure (SCP). (2024)
Journal Article
IBUKUN, M., ELYAN, E., AMISH, M., NJUGUNA, J. and OLUYEMI, G.F. 2024. A review of well life cycle integrity challenges in the oil and gas industry and its implications for sustained casing pressure (SCP). Energies [online], 17(22), article number 5562. Available from: https://doi.org/10.3390/en17225562

Sustained Casing Pressure (SCP) is a condition in oil and gas wells where continuous pressure buildup in the well casing over a long period of time occurs. Several factors might be responsible for this, including the influx of formation fluids, the l... Read More about A review of well life cycle integrity challenges in the oil and gas industry and its implications for sustained casing pressure (SCP)..

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..

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..

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..

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..

A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews. (2023)
Journal Article
MORENO-GARCIA, C.F., JAYNE, C., ELYAN, E. and ACEVES-MARTINS, M. 2023. A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews. Decision analytics journal [online], 6, article 100162. Available from: https://doi.org/10.1016/j.dajour.2023.100162

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior training. This is possible by teaching the system how to codify a question and find its answer in the text. In many domains, especially he... Read More about A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews..

Panchromatic and multispectral image fusion for remote sensing and earth observation: concepts, taxonomy, literature review, evaluation methodologies and challenges ahead. (2023)
Journal Article
ZHANG, K., ZHANG, F., WAN, W., YU, H., SUN, J., DEL SER, J., ELYAN, E. and HUSSAIN, A. 2023. Panchromatic and multispectral image fusion for remote sensing and earth observation: concepts, taxonomy, literature review, evaluation methodologies and challenges ahead. Information fusion [online], 93, pages 227-242. Available from: https://doi.org/10.1016/j.inffus.2022.12.026

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks c... Read More about Panchromatic and multispectral image fusion for remote sensing and earth observation: concepts, taxonomy, literature review, evaluation methodologies and challenges ahead..

Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. (2022)
Journal Article
SARKER, M.M.K., AKRAM, F., ALSHARID, M., SINGH, V.K., YASRAB, R. and ELYAN, E. 2023. Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. Diagnostics [online], 13(1), article 103. Available from: https://doi.org/10.3390/diagnostics13010103

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtyp... Read More about Efficient breast cancer classification network with dual squeeze and excitation in histopathological images..

Fusion methods for face presentation attack detection. (2022)
Journal Article
ABDULLAKUTTY, F., JOHNSTON, P. and ELYAN, E. 2022. Fusion methods for face presentation attack detection. Sensors [online], 22(14): soft sensors 2021-2022, article 5196. Available from: https://doi.org/10.3390/s22145196

Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literat... Read More about Fusion methods for face presentation attack detection..

Deep transfer learning on the aggregated dataset for face presentation attack detection. (2022)
Journal Article
ABDULLAKUTTY, F., ELYAN, E., JOHNSTON, P. and ALI-GOMBE, A. 2022. Deep transfer learning on the aggregated dataset for face presentation attack detection. Cognitive computation [online], 14(6), pages 2223-2233. Available from: https://doi.org/10.1007/s12559-022-10037-z

Presentation attacks are becoming a serious threat to one of the most common biometric applications, namely face recognition (FR). In recent years, numerous methods have been presented to detect and identify these attacks using publicly available dat... Read More about Deep transfer learning on the aggregated dataset for face presentation attack detection..

The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature. (2022)
Journal Article
GUMBS, A.A., GRASSO, V., BOURDEL, N., CRONER, R., SPOLVERATO, G., FRIGERIO, I., ILLANES, A., ABU HILAL, M., PARK, A. and ELYAN, E. 2022. The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature. Sensors [online], 22(13), article 4918. Available from: https://doi.org/10.3390/s22134918

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, "Artificial I... Read More about The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature..