Outputs (83)

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], Latest Articles. 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. [Article] (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], In Press. 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. [Article].

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)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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)
Conference Proceeding
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..

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..

Collective behaviour monitoring in football using spatial temporal and network analysis: application and evaluations. (2022)
Thesis
CORSIE, M.R. 2022. Collective behaviour monitoring in football using spatial temporal and network analysis: application and evaluations. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1880207

Analysis is an important part of understanding and exploiting performance of football teams. Traditional approaches of analysis have centred around events that may not fully incorporate the highly dynamic nature of matches. To circumvent this weaknes... Read More about Collective behaviour monitoring in football using spatial temporal and network analysis: application and evaluations..

Cross domain evaluation of text detection models. (2022)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCÍA, C. and JAYNE, C. 2022. Cross domain evaluation of text detection models. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A. and Aydin, M. (eds.) Artificial neural networks and machine learning - ICANN 2022: proceedings of the 31st International conference on artificial neural networks (ICANN 2022), 6-9 September 2022, Bristol, UK, part III. Lecture notes in computer science, 13531. Cham: Springer [online], pages 50-61. Available from: https://doi.org/10.1007/978-3-031-15934-3_5

Text detection is a very common task across a wide range of domains, such as document image analysis, remote identity verification, amongst others. It is also considered an integral component of any text recognition system, where the performance of r... Read More about Cross domain evaluation of text detection models..

TransSLC: skin lesion classification in dermatoscopic images using transformers. (2022)
Conference Proceeding
SARKER, M.M.K., MORENO-GARCÍA, C.F., REN, J. and ELYAN, E. 2022. TransSLC: skin lesion classification in dermatoscopic images using transformers. In Yang, G., Aviles-Rivero, A., Roberts, M. and Schönlieb, C.-B. (eds.) Medical image understanding and analysis: proceedings of 26th Medical image understanding and analysis 2022 (MIUA 2022), 27-29 July 2022, Cambridge, UK. Lecture notes in computer sciences, 13413. Cham: Springer [online], pages 651-660. Available from: https://doi.org/10.1007/978-3-031-12053-4_48

Early diagnosis and treatment of skin cancer can reduce patients' fatality rates significantly. In the area of computer-aided diagnosis (CAD), the Convolutional Neural Network (CNN) has been widely used for image classification, segmentation, and rec... Read More about TransSLC: skin lesion classification in dermatoscopic images using transformers..

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..

A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams. (2021)
Conference Proceeding
TORAL, L., MORENO-GARCIA, C.F., ELYAN, E. and MEMON, S. 2021. A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams. In Barney Smith, E.H. and Pal, U. (eds.) Document analysis and recognition: ICDAR 2021 workshops, part II: proceedings of 16th International conference on document analysis and recognition 2021 (ICDAR 2021), 5-10 September 2021, Lausanne, Switzerland. Lecture notes in computer science, 12917. Cham: Springer [online], pages 268-276. Available from: https://doi.org/10.1007/978-3-030-86159-9_18

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In th... Read More about A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams..

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?.

Class-decomposition and augmentation for imbalanced data sentiment analysis. (2021)
Conference Proceeding
MORENO-GARCIA, C.F., JAYNE, C. and ELYAN, E. 2021. Class-decomposition and augmentation for imbalanced data sentiment analysis. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533603. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533603

Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's... Read More about Class-decomposition and augmentation for imbalanced data sentiment analysis..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

Face detection with YOLO on edge. (2021)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCIA, C.F. and ZWIEGELAAR, J. 2021. Face detection with YOLO on edge. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 284-292. Available from: https://doi.org/10.1007/978-3-030-80568-5_24

Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learnin... Read More about Face detection with YOLO on edge..

The utility of mathematical fitness-fatigue models for assisting with the planning of physical training for sport: from in silico experiments employing synthetic data, lower-bound operational conditions and model estimation, to the development of software resources for future research. (2021)
Thesis
STEPHENS HEMINGWAY, B.H. 2021. The utility of mathematical fitness-fatigue models for assisting with the planning of physical training for sport: from in silico experiments employing synthetic data, lower-bound operational conditions and model estimation, to the development of software resources for future research. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1603154

The greatest potential application of mathematical models in sport science is to predict future performance of individual athletes in response to training, with sufficient accuracy to assist with planning of training programs and short tapering perio... Read More about The utility of mathematical fitness-fatigue models for assisting with the planning of physical training for sport: from in silico experiments employing synthetic data, lower-bound operational conditions and model estimation, to the development of software resources for future research..

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..

Two layer ensemble of deep learning models for medical image segmentation. [Preprint] (2021)
Working Paper
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2021. Two layer ensemble of deep learning models for medical image segmentation. arXiv [online]. Available from: https://doi.org/10.48550/arXiv.2104.04809

In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further improve the... Read More about Two layer ensemble of deep learning models for medical image segmentation. [Preprint].

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..

Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification. (2020)
Thesis
VUTTIPITTAYAMONGKOL, P. 2020. Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://openair.rgu.ac.uk

Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not d... Read More about Learning from class-imbalanced data: overlap-driven resampling for 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.”.

Deep learning for text detection and recognition in complex engineering diagrams. (2020)
Conference Proceeding
JAMIESON, L, MORENO-GARCIA, C.F. and ELYAN, E. 2020. Deep learning for text detection and recognition in complex engineering diagrams. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207127. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207127

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper... Read More about Deep learning for text detection and recognition in complex engineering diagrams..

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..

Overlap-based undersampling method for classification of imbalanced medical datasets. (2020)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Overlap-based undersampling method for classification of imbalanced medical datasets. In Maglogiannis, I., Iliadis, L. and Pimenidis, E. (eds.) Artificial intelligence applications and innovations: AIAI 2020; proceedings of 16th International Federation for Information Processing working group (IFIP WG) 12.5 International artificial intelligence applications and innovations, 5-7 June 2020, Halkidiki, Greece. IFIP advances in information and communication technology, 584. Cham: Springer [online], pages 358-369. Available from: https://doi.org/10.1007/978-3-030-49186-4_30

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and... Read More about Overlap-based undersampling method for classification of imbalanced medical datasets..

Towards a reliable face recognition system. (2020)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E. and ZWIEGELAAR, J. 2020. Towards a reliable face recognition system. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 304-316. Available from: https://doi.org/10.1007/978-3-030-48791-1_23

Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the performance of models to human-level accuracy. However, the va... Read More about Towards a reliable face recognition system..

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. (2020)
Conference Proceeding
ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents... Read More about Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks..

Predicting permeability based on core analysis. (2020)
Conference Proceeding
KONTOPOULOS, H., AHRIZ, H., ELYAN, E. and ARNOLD, R. 2020. Predicting permeability based on core analysis. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, vol 2. Cham: Springer [online], pages 143-154. Available from: https://doi.org/10.1007/978-3-030-48791-1_10

Knowledge of permeability, a measure of the ability of rocks to allow fluids to flow through them, is essential for building accurate models of oil and gas reservoirs. Permeability is best measured in the laboratory using special core analysis (SCAL)... Read More about Predicting permeability based on core analysis..

Augmented reality procedural guide system. [Video recording] (2020)
Digital Artefact
ELYAN, E. 2020. Augmented reality procedural guide system. [Video recording]. Aberdeen: Robert Gordon University [online]. Available from: https://youtu.be/SM15leaKbJY

This video provides a brief demonstration of an augmented reality (AR) system for the provision of guidance on correct procedures during the handling of complex equipment. This system was created as part of a project that aimed to replace user guides... Read More about Augmented reality procedural guide system. [Video recording].

Learning from small and imbalanced dataset of images using generative adversarial neural networks. (2019)
Thesis
ALI-GOMBE, A. 2019. Learning from small and imbalanced dataset of images using generative adversarial neural networks. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a ma... Read More about Learning from small and imbalanced dataset of images using generative adversarial neural networks..

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..

Multiple fake classes GAN for data augmentation in face image dataset. (2019)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E. and JAYNE, C. 2019. Multiple fake classes GAN for data augmentation in face image dataset. In Proceedings of the 2019 International joint conference on neural networks (IJCNN 2019), 14-19 July 2019, Budapest, Hungary. Piscataway: IEEE [online], article ID 8851953. Available from: https://doi.org/10.1109/IJCNN.2019.8851953

Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm o... Read More about Multiple fake classes GAN for data augmentation in face image dataset..

Digitisation of assets from the oil and gas industry: challenges and opportunities. (2019)
Conference Proceeding
MORENO-GARCIA, C.F. and ELYAN, E. 2019. Digitisation of assets from the oil and gas industry: challenges and opportunities. In Proceedings of 2019 International conference on document analysis and recognition workshops (ICDARW), 22-25 September 2019, Sydney, Australia. Piscataway: IEEE [online], 7, pages 2-5. Available from: https://doi.org/10.1109/ICDARW.2019.60122

Automated processing and analysis of legacies of printed documents across the Oil & Gas industry provide a unique opportunity and at the same time pose a significant challenge. One particular example is the case of Piping and Instrumentation Diagrams... Read More about Digitisation of assets from the oil and gas industry: challenges and opportunities..

Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. (2019)
Journal Article
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Information sciences [online], 509, pages 47-70. Available from: https://doi.org/10.1016/j.ins.2019.08.062

Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning ta... Read More about Neighbourhood-based undersampling approach for handling imbalanced and overlapped data..

Beyond the pixels: learning and utilising video compression features for localisation of digital tampering. (2019)
Thesis
JOHNSTON, P. 2019. Beyond the pixels: learning and utilising video compression features for localisation of digital tampering. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Video compression is pervasive in digital society. With rising usage of deep convolutional neural networks (CNNs) in the fields of computer vision, video analysis and video tampering detection, it is important to investigate how patterns invisible to... Read More about Beyond the pixels: learning and utilising video compression features for localisation of digital tampering..

Learning to self-manage by intelligent monitoring, prediction and intervention. (2019)
Conference Proceeding
WIRATUNGA, N., CORSAR, D., MARTIN, K., WIJEKOON, A., ELYAN, E., COOPER, K., IBRAHIM, Z., CELIKTUTAN, O., DOBSON, R.J., MCKENNA, S., MORRIS, J., WALLER, A., ABD-ALHAMMED, R., QAHWAJI, R. and CHAUDHURI, R. 2019. Learning to self-manage by intelligent monitoring, prediction and intervention. In Wiratunga, N., Coenen, F. and Sani, S. (eds.) Proceedings of the 4th International workshop on knowledge discovery in healthcare data (KDH 2019), co-located with the 28th International joint conference on artificial intelligence (IJCAI-19), 10-11 August 2019, Macao, China. CEUR workshop proceedings, 2429. Aachen: CEUR-WS [online], pages 60-67. Available from: http://ceur-ws.org/Vol-2429/paper10.pdf

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of out-of-hospital care requires researchers to expand their horizons; integrated assistive technologies sho... Read More about Learning to self-manage by intelligent monitoring, prediction and intervention..

MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. (2019)
Journal Article
ALI-GOMBE, A. and ELYAN, E. 2019. MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing [online], 361, pages 212-221. Available from: https://doi.org/10.1016/j.neucom.2019.06.043

Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data Augmentation is a common approach tha... Read More about MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network..

Video tampering localisation using features learned from authentic content. (2019)
Journal Article
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2020. Video tampering localisation using features learned from authentic content. Neural computing and applications [online], 32(16): special issue on Real-world optimization problems and meta-heuristics and selected papers from the 19th Engineering applications of neural networks conference 2018 (EANN 2018), 3-5 September 2018, Bristol UK , pages 12243-12257. Available from: https://doi.org/10.1007/s00521-019-04272-z

Video tampering detection remains an open problem in the field of digital media forensics. As video manipulation techniques advance, it becomes easier for tamperers to create convincing forgeries that can fool human eyes. Deep learning methods have a... Read More about Video tampering localisation using features learned from authentic content..

A review of digital video tampering: from simple editing to full synthesis. (2019)
Journal Article
JOHNSTON, P. and ELYAN, E. 2019. A review of digital video tampering: from simple editing to full synthesis. Digital investigation [online], 29, pages 67-81. Available from: https://doi.org/10.1016/j.diin.2019.03.006

Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historica... Read More about A review of digital video tampering: from simple editing to full synthesis..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

Toward video tampering exposure: inferring compression parameters from pixels. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Toward video tampering exposure: inferring compression parameters from pixels. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 44-57, Available from: https://doi.org/10.1007/978-3-319-98204-5_4

Video tampering detection remains an open problem in the field of digital media forensics. Some existing methods focus on recompression detection because any changes made to the pixels of a video will require recompression of the complete stream. Rec... Read More about Toward video tampering exposure: inferring compression parameters from pixels..

Deep imitation learning with memory for robocup soccer simulation. (2018)
Conference Proceeding
HUSSEIN, A., ELYAN, E. and JAYNE, C. 2018. Deep imitation learning with memory for robocup soccer simulation. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 31-43. Available from: https://doi.org/10.1007/978-3-319-98204-5_3

Imitation learning is a field that is rapidly gaining attention due to its relevance to many autonomous agent applications. Providing demonstrations of effective behaviour to teach the agent is useful in real world challenges such as sparse rewards a... Read More about Deep imitation learning with memory for robocup soccer simulation..

Symbols classification in engineering drawings. (2018)
Conference Proceeding
ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we p... Read More about Symbols classification in engineering drawings..

Spatial effects of video compression on classification in convolutional neural networks. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Spatial effects of video compression on classification in convolutional neural networks. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489370. Available from: https://doi.org/10.1109/IJCNN.2018.8489370

A collection of Computer Vision application reuse pre-learned features to analyse video frame-by-frame. Those features are classically learned by Convolutional Neural Networks (CNN) trained on high quality images. However, available video content is... Read More about Spatial effects of video compression on classification in convolutional neural networks..

Few-shot classifier GAN. (2018)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., SAVOYE, Y. and JAYNE, C. 2018. Few-shot classifier GAN. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489387. Available from: https://doi.org/10.1109/IJCNN.2018.8489387

Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for fe... Read More about Few-shot classifier GAN..

New trends on digitisation of complex engineering drawings. (2018)
Journal Article
MORENO-GARCIA, C.F., ELYAN, E. and JAYNE, C. 2019. New trends on digitisation of complex engineering drawings. Neural computing and applications [online], 31(6): selected papers from the proceedings of the 18th Engineering applications of neural networks conference (EANN 2017), 25-27 August 2017, Athens, Greece, pages 1695-1712. Available from: https://doi.org/10.1007/s00521-018-3583-1

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that m... Read More about New trends on digitisation of complex engineering drawings..

Deep learning based approaches for imitation learning. (2018)
Thesis
HUSSEIN, A. 2018. Deep learning based approaches for imitation learning. Robert Gordon University, PhD thesis.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for int... Read More about Deep learning based approaches for imitation learning..

Cognitive modelling and control of human error processes in human-computer interaction with safety critical IT systems in telehealth. (2017)
Thesis
ALWAWI, I. 2017. Cognitive modelling and control of human error processes in human-computer interaction with safety critical IT systems in telehealth. Robert Gordon University, PhD thesis.

The field of telehealth has developed rapidly in recent years. It provides medical support particularly to those who are living in remote areas and in emergency cases. Although developments in both technology and practice have been rapid, there are s... Read More about Cognitive modelling and control of human error processes in human-computer interaction with safety critical IT systems in telehealth..

Deep imitation learning for 3D navigation tasks. (2017)
Journal Article
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2018. Deep imitation learning for 3D navigation tasks. Neural computing and applications [online], 29(7), pages 389-404. Available from: https://doi.org/10.1007/s00521-017-3241-z

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imita... Read More about Deep imitation learning for 3D navigation tasks..

The effect of person order on egress time: a simulation model of evacuation from a neolithic visitor attraction. (2017)
Journal Article
STEWART, A., ELYAN, E., ISAACS, J., MCEWEN, L. and WILSON, L. 2017. The effect of person order on egress time: a simulation model of evacuation from a neolithic visitor attraction. Human factors [online], 59(8), pages 1222-1232. Available from: https://doi.org/10.1177/0018720817729608

Objective: The aim of this study was to model the egress of visitors from a Neolithic visitor attraction. Background: Tourism attracts increasing numbers of elderly and mobility-impaired visitors to our built-environment heritage sites. Some such sit... Read More about The effect of person order on egress time: a simulation model of evacuation from a neolithic visitor attraction..

Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings. (2017)
Conference Proceeding
MORENO-GARCÍA, C.F., ELYAN, E. and JAYNE, C. 2017. Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings. In Boracchi, G., Iliadis, L., Jayne, C. and Likas, A. (eds.) Engineering applications of neural networks: proceedings of the 18th International engineering applications of neural networks (EANN 2017), 25-27 August 2017, Athens, Greece. Communications in computer and information science, 744. Cham: Springer [online], pages 87-98. Available from: https://doi.org/10.1007/978-3-319-65172-9_8

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this... Read More about Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings..

Deep reward shaping from demonstrations. (2017)
Conference Proceeding
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2017. Deep reward shaping from demonstrations. In Proceedings of the 2017 International joint conference on neural networks (IJCNN 2017), 14-19 May 2017, Anchorage, USA. Piscataway: IEEE [online], article number 7965896, pages 510-517. Available from: https://doi.org/10.1109/IJCNN.2017.7965896

Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of problems. The combination of deep learning and reinforcement learning allows for a generic learning process that does not consider specific knowledge of... Read More about Deep reward shaping from demonstrations..

Imitation learning: a survey of learning methods. (2017)
Journal Article
HUSSEIN, A., GABER, M.M., ELYAN, E. and JAYNE, C. 2017. Imitation learning: a survey of learning methods. ACM computing surveys [online], 50(2), article 21. Available from: https://doi.org/10.1145/3054912

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has be... Read More about Imitation learning: a survey of learning methods..

On pruning and feature engineering in Random Forests. (2016)
Thesis
FAWAGREH, K. 2016. On pruning and feature engineering in Random Forests. Robert Gordon University, PhD thesis.

Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is stil... Read More about On pruning and feature engineering in Random Forests..

An outlier ranking tree selection approach to extreme pruning of random forests. (2016)
Conference Proceeding
FAWAGREH, K., GABER, M.M. and ELYAN, E. 2016. An outlier ranking tree selection approach to extreme pruning of random forests. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 267-282. Available from: https://doi.org/10.1007/978-3-319-44188-7_20

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still ro... Read More about An outlier ranking tree selection approach to extreme pruning of random forests..

Deep active learning for autonomous navigation. (2016)
Conference Proceeding
HUSSEIN, A., GABER, M.M. and ELYAN, E. 2016. Deep active learning for autonomous navigation. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 3-17. Available from: https://doi.org/10.1007/978-3-319-44188-7_1

Imitation learning refers to an agent's ability to mimic a desired behavior by learning from observations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficien... Read More about Deep active learning for autonomous navigation..

A genetic algorithm approach to optimising random forests applied to class engineered data. (2016)
Journal Article
ELYAN, E. and GABER, M.M. 2017. A genetic algorithm approach to optimising random forests applied to class engineered data. Information sciences [online], 384, pages 220-234. Available from: https://doi.org/10.1016/j.ins.2016.08.007

In numerous applications and especially in the life science domain, examples are labelled at a higher level of granularity. For example, binary classification is dominant in many of these datasets, with the positive class denoting the existence of a... Read More about A genetic algorithm approach to optimising random forests applied to class engineered data..

3D anthropometry: quantifying the shape and size variability within the UK male offshore oil and gas workforce. (2016)
Thesis
LEDINGHAM, R.J. 2016. 3D anthropometry: quantifying the shape and size variability within the UK male offshore oil and gas workforce. Robert Gordon University, MRes thesis.

Background: UK male offshore workers typically increased in weight by 19% since 1985, and are also heavier than the background UK male population. Aim: To conduct an anthropometric survey on UK offshore workers, employing the latest portable 3D scann... Read More about 3D anthropometry: quantifying the shape and size variability within the UK male offshore oil and gas workforce..

A fine-grained Random Forests using class decomposition: an application to medical diagnosis. (2015)
Journal Article
ELYAN, E. and GABER, M.M. 2015. A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], 27(8), pages 2279-2288. Available from: https://doi.org/10.1007/s00521-015-2064-z

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, espe... Read More about A fine-grained Random Forests using class decomposition: an application to medical diagnosis..

Twitter response to televised political debates in Election 2015. (2015)
Book Chapter
PEDERSEN, S., BAXTER, G., BURNETT, S., MACLEOD, I., GOKER, A., HERON, M., ISAACS, J., ELYAN, E. and KALICIAK, L. 2015. Twitter response to televised political debates in Election 2015. In Jackson, D. and Thorsen, E. (eds.) UK election analysis 2015: media, voters and the campaign: early reflections from leading UK academics. Poole: Bournemouth University, centre for the study of journalism, culture and community [online], page 73. Available from: http://www.electionanalysis.uk/uk-election-analysis-2015/section-6-social-media/twitter-response-to-televised-political-debates-in-election-2015/

The advent of social media such as Twitter has revolutionised our conversations about live television events. In the days before the Internet, conversation about television programmes was limited to those sitting on the sofa with you and people you m... Read More about Twitter response to televised political debates in Election 2015..

On the relationship between variational level set-based and SOM-based active contours. (2015)
Journal Article
ABDELSAMEA, M.M., GNECCO, G., GABER, M.M. and ELYAN, E. 2015. On the relationship between variational level set-based and SOM-based active contours. Computational intelligence and neuroscience [online], 2015, article ID 109029. Available from:https://doi.org/10.1155/2015/109029

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods hav... Read More about On the relationship between variational level set-based and SOM-based active contours..

Content based video retrieval via spatial-temporal information discovery. (2013)
Thesis
WANG, L. 2013. Content based video retrieval via spatial-temporal information discovery. Robert Gordon University, PhD thesis.

Content based video retrieval (CBVR) has been strongly motivated by a variety of realworld applications. Most state-of-the-art CBVR systems are built based on Bag-of-visual- Words (BovW) framework for visual resources representation and access. The f... Read More about Content based video retrieval via spatial-temporal information discovery..

An investigation into the cognitive effects of instructional interface visualisations. (2013)
Thesis
AKINLOFA, O.R. 2013. An investigation into the cognitive effects of instructional interface visualisations. Robert Gordon University, PhD thesis.

An investigation is conducted into the cognitive effects of using different computer based instructions media in acquisition of specific novel human skills. With recent rapid advances in computing and multimedia instructional delivery, several contem... Read More about An investigation into the cognitive effects of instructional interface visualisations..

Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval. (2012)
Conference Proceeding
WANG, L., SONG, D. and ELYAN, E. 2012. Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval. In Proceedings of the 21st Association for Computing Machinery (ACM) international conference on information and knowledge management (CIKM'12), 29 October - 2 November 2012, Maui, USA. New York: ACM [online], pages 1303-1312. Available from: https://doi.org/10.1145/2396761.2398433

Most of the state-of-art approaches to Query-by-Example (QBE) video retrieval are based on the Bag-of-visual-Words (BovW) representation of visual content. It, however, ignores the spatial-temporal information, which is important for similarity measu... Read More about Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval..

Automatic features characterization from 3d facial images. (2010)
Conference Proceeding
ELYAN, E. and UGAIL, H. 2010. Automatic features characterization from 3d facial images. In Arabnia, H.R., Deligiannidis, L. and Solo, A.M.G. (eds.) Proceedings of the 14th International computer graphics and virtual reality conference (CGVR 2010), 12-15 July 2010, Las Vegas, USA. Georgia, USA: CSREA Press, pages 67-73.

This paper presents a novel and computationally fast method for automatic identification of symmetry profile from 3D facial images. The algorithm is based on the concepts of computational geometry which yield fast and accurate results. In order to de... Read More about Automatic features characterization from 3d facial images..

Interactive surface design and manipulation using PDE-method through Autodesk Maya plug-in. (2009)
Conference Proceeding
ELYAN, E. and UGAIL, H. 2009. Interactive surface design and manipulation using PDE-method through Autodesk Maya plug-in. In Ugail, H., Qahwaji, R.S.R, Earnshaw, R.A. and Willis, P.J. (eds.) Proceedings of the 2009 International conference on Cyberworlds (CYBERWORLDS 2009), 7-11 September 2009, Bradford, UK. Los Alamitos: IEEE Computer Society [online], article number 5279653, pages 119-125. Available from: https://doi.org/10.1109/CW.2009.49

This paper aims to propose a method for geometric design, modelling and shape manipulation using minimum input design parameters. Here, we address the method for the construction of 3D geometry based on the use of Elliptic Partial Differential Equati... Read More about Interactive surface design and manipulation using PDE-method through Autodesk Maya plug-in..

Automatic 3D face recognition using Fourier descriptors. (2009)
Conference Proceeding
ELYAN, E. and UGAIL, H. 2009. Automatic 3D face recognition using Fourier descriptors. In Ugail, H., Qahwaji, R.S.R, Earnshaw, R.A. and Willis, P.J. (eds.) Proceedings of the 2009 International conference on Cyberworlds (CYBERWORLDS 2009), 7-11 September 2009, Bradford, UK. Los Alamitos: IEEE Computer Society [online], article number 5279600, pages 246-252. Available from: https://doi.org/10.1109/CW.2009.48

3D face recognition is attracting more attention due to the recent development in 3D facial data acquisition techniques. It is strongly believed that 3D Face recognition systems could overcome the inherent problems of 2D face recognition such as faci... Read More about Automatic 3D face recognition using Fourier descriptors..