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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..

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. (2022)
Journal Article
ELYAN, E., VUTTIPITTAYAMONGKOL, P., JOHNSTON, P., MARTIN, K., MCPHERSON, K., MORENO-GARCIA, C.F., JAYNE, C. and SARKER, M.M.K. 2022. Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial intelligence surgery [online], 2, pages 24-25. Available from: https://doi.org/10.20517/ais.2021.15

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmen... Read More about Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward..

Antimicrobial resistance and machine learning: challenges and opportunities. (2022)
Journal Article
ELYAN, E., HUSSAIN, A., SHEIKH, A., ELMANAMA, A.A., VUTTPITTAYAMONGKOL, P. and HIJAZI, K. 2022. Antimicrobial resistance and machine learning: challenges and opportunities. IEEE access [online], 10, pages 31561-31577. Available from: https://doi.org/10.1109/ACCESS.2022.3160213

Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), wher... Read More about Antimicrobial resistance and machine learning: challenges and opportunities..

Psychosocial impact of 8 weeks COVID-19 quarantine on Italian parents and their children. (2022)
Journal Article
KHOORY, B.J., KEUNING, M.W., FLEDDERUS, A.C., CICCHELLI, R., FANOS, V., KHOORY, J., NERVI, D., ELYAN, E., VUTTIPITTAYAMONGKOL, P., OOMEN, M.W.N., PAJKRT, P. and ABU HILAL, M. 2022. Psychosocial impact of 8 weeks COVID-19 quarantine on Italian parents and their children. Maternal and child health journal [online], 26(6), pages 1312-1321. Available from: https://doi.org/10.1007/s10995-021-03311-3

Objectives: Italy was affected greatly by Coronavirus disease 2019 (COVID-19), emerging mainly in the Italian province of Lombardy. This outbreak led to profound governmental interventions along with a strict quarantine. This quarantine may have psyc... Read More about Psychosocial impact of 8 weeks COVID-19 quarantine on Italian parents and their children..

A data-driven decision support tool for offshore oil and gas decommissioning. (2021)
Journal Article
VUTTIPITTAYAMONGKOL, P., TUNG, A. and ELYAN, E. 2021. A data-driven decision support tool for offshore oil and gas decommissioning. IEEE access [online], 9, pages 137063-137082. Available from: https://doi.org/10.1109/ACCESS.2021.3117891

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exh... Read More about A data-driven decision support tool for offshore oil and gas decommissioning..

Artificial intelligence surgery: how do we get to autonomous actions in surgery? (2021)
Journal Article
GUMBS, A.A., FRIGERIO, I., SPOLVERATO, G., CRONER, R., ILLANES, A., CHOUILLARD, E. and ELYAN, E. 2021. Artificial intelligence surgery: how do we get to autonomous actions in surgery? Sensors [online], 21(16), article 5526. Available from: https://doi.org/10.3390/s21165526

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intel... Read More about Artificial intelligence surgery: how do we get to autonomous actions in surgery?.

A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods. (2021)
Journal Article
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2021. A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods. Information fusion [online], 75, pages 55-69. Available from: https://doi.org/10.1016/j.inffus.2021.04.015

Face Recognition is considered one of the most common biometric solutions these days and is widely used across a range of devices for various security purposes. The performance of FR systems has improved by orders of magnitude over the past decade. T... Read More about A review of state-of-the-art in face presentation attack detection: from early development to advanced deep learning and multi-modal fusion methods..

Burst detection-based selective classifier resetting. (2021)
Journal Article
WARES, S., ISAACS, J. and ELYAN, E. 2021. Burst detection-based selective classifier resetting. Journal of information and knowledge management [online], 20(2), article 2150027. Available from: https://doi.org/10.1142/S0219649221500271

Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a... Read More about Burst detection-based selective classifier resetting..

On the class overlap problem in imbalanced data classification. (2020)
Journal Article
VUTTIPITTAYAMONGKOL, P., ELYAN, E. and PETROVSKI, A. 2021. On the class overlap problem in imbalanced data classification. Knowledge-based systems [online], 212, article number 106631. Available from: https://doi.org/10.1016/j.knosys.2020.106631

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed criti... Read More about On the class overlap problem in imbalanced data classification..

Response to discussion on “Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson’s disease.” (2020)
Journal Article
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Response to discussion on “Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson’s disease.”. International journal of neural systems [online], 30(9), article ID 2075002. Available from: https://doi.org/10.1142/s0129065720750027

In the paper 'Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease', the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The... Read More about Response to discussion on “Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson’s disease.”.

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. (2020)
Journal Article
ELYAN, E., MORENO-GARCIA, C.F. and JAYNE, C. 2021. CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural computing and applications [online], 33(7), pages 2839-2851. Available from: https://doi.org/10.1007/s00521-020-05130-z

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets re... Read More about CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification..

Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease. (2020)
Journal Article
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease. International journal of neural systems [online], 30(8), article ID 2050043. Available from: https://doi.org/10.1142/S0129065720500434

Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced da... Read More about Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease..

Deep learning for symbols detection and classification in engineering drawings. (2020)
Journal Article
ELYAN, E., JAMIESON, L. and ALI-GOMBE, A. 2020. Deep learning for symbols detection and classification in engineering drawings. Neural networks [online], 129, pages 91-102. Available from: https://doi.org/10.1016/j.neunet.2020.05.025

Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practic... Read More about Deep learning for symbols detection and classification in engineering drawings..

The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach. (2019)
Journal Article
STEPHENS HEMINGWAY, B.H., BURGESS, K.E., ELYAN, E. and SWINTON, P.A. 2020. The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach. International journal of sports science and coaching [online], 15(1), pages 60-71. Available from: https://doi.org/10.1177/1747954119887721

This study investigated the effects of measurement error and testing frequency on prediction accuracy of the standard fitness-fatigue model. A simulation-based approach was used to systematically assess measurement error and frequency inputs commonly... Read More about The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach..

Data stream mining: methods and challenges for handling concept drift. (2019)
Journal Article
WARES, S., ISAACS, J. and ELYAN, E. 2019. Data stream mining: methods and challenges for handling concept drift. SN applied sciences [online], 1(11), article ID 1412. Available from: https://doi.org/10.1007/s42452-019-1433-0

Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of... Read More about Data stream mining: methods and challenges for handling concept drift..