A multimodel-based screening framework for C-19 using deep learning-inspired data fusion.

Shankar, Achyut; Rizwan, P.; Mekala, M.S.; Elyan, Eyad; Gandomi, Amir H.; Maple, Carsten; Rodrigues, Joel J.P.C.

doi:10.1109/jbhi.2024.3400878

Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. (2025)
Journal Article
EKEKE, I., MORENO-GARCÍA, C.F. and ELYAN, E. 2025. Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. Applied artificial intelligence [online], 39(1), article number 2548834. Available from: https://doi.org/10.1080/08839514.2025.2548834

The digitization of electrical diagrams plays a crucial role in modern construction industries, enabling efficient reuse, seamless distribution, and accurate archiving. Despite technological advances, many of these diagrams remain in undigitized form... Read More about Attention-based framework for automated symbol recognition and wiring design in electrical diagrams..

Modified CBAM: sub-block pooling for improved channel and spatial attention. (2025)
Presentation / Conference Contribution
HUSSAINI, H., BANO, S., ELYAN, E. and MORENO-GARCIA, C.F. 2026. Modified CBAM: sub-block pooling for improved channel and spatial attention. In Ali, S., Hogg, D.C. and Peckham, M. (eds.) Proceedings of the 29th Annual conference of the Medical image understanding and analysis 2025 (MIUA 2025), 15-17 July 2025, Leeds, UK. Lecture notes in computer science, 15917. Cham: Springer [online], part II, pages 116-130. Available from: https://doi.org/10.1007/978-3-031-98691-8_9

The Convolutional Block Attention Module (CBAM) has emerged as a widely adopted attention mechanism, as it seamlessly integrates into the Convolutional Neural Network (CNN) architecture with minimal computational overhead. However, its reliance on gl... Read More about Modified CBAM: sub-block pooling for improved channel and spatial attention..

An evolutionary neural architecture search-based approach for time series forecasting. (2025)
Presentation / Conference Contribution
VU, T.H., NGUYEN, T.T. and ELYAN, E. 2025. An evolutionary neural architecture search-based approach for time series forecasting. In Proceedings of the 2025 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2025), 8-12 June 2025, Hangzhou, China. Piscataway: IEEE [online], article number 11043002, pages 1-8. Available from: https://doi.org/10.1109/cec65147.2025.11043002

Time series forecasting (TSF) is one of the most prevalent research topics in artificial intelligence and has garnered significant attention in the research community. In recent years, significant breakthroughs have been made in TSF research, shiftin... Read More about An evolutionary neural architecture search-based approach for time series forecasting..

CEANet: an end-to-end transformer based static-mobile points alignment approach. (2025)
Journal Article
YESIPOV, V., MEKALA, M.S., EDGE, P. and ELYAN, E. 2025. CEANet: an end-to-end transformer based static-mobile points alignment approach. Signal, image and video processing [online], 19(8), article number 596. Available from: https://doi.org/10.1007/s11760-025-04258-6

Correspondence-based Point Cloud Registration (PCR) is crucial for 3D visualization applications, especially in change detection. Most PCR models depend on precise initialization using a set of closest points to establish correspondences, a method th... Read More about CEANet: an end-to-end transformer based static-mobile points alignment approach..

A feature transformation technique for improving ensemble learning systems. (2025)
Presentation / Conference Contribution
NGUYEN, T.T., VU, H., DANG, T., ELYAN, E., VU, T.S. and NGUYEN, T.T. 2025. A feature transformation technique for improving ensemble learning systems. In Nguyen, N.T., Matsuo, T., Gaol, F.L. et al (eds.) Intelligent information and database systems: proceedings of the 17th Asian conference on intelligent information database systems 2025 (ACIIDS 2025), 23-25 April 2025, Kitakyushu, Japan. Lecture notes in computer science, 15684. Singapore: Springer [online], part II, pages 292-306. Available from: https://doi.org/10.1007/978-981-96-6005-6_21

In this study, we propose a feature transformation approach to improve the performance of Ensemble Learning Systems. Our method operates on the predictions of base classifiers within an ensemble system, known as meta-data, which are produced by apply... Read More about A feature transformation technique for improving ensemble learning systems..

Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. [Dataset] (2025)
Data
EKEKE, I., MORENO-GARCÍA, C.F. and ELYAN, E. 2025. Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. [Dataset]. Hosted on Figshare [online]. Available from: https://doi.org/10.6084/m9.figshare.28726547

The research presents an end-to-end deep learning framework combining YOLOv8 object detection with attention mechanisms to improve symbol recognition in electrical diagrams, followed by a graph-based wiring algorithm that automates wire routing betwe... Read More about Attention-based framework for automated symbol recognition and wiring design in electrical diagrams. [Dataset].

Advanced modelling and analysis for quality assessment and enhancement of underwater multimodal imageries. (2025)
Thesis
TOLIE, H.F. 2025. Advanced modelling and analysis for quality assessment and enhancement of underwater multimodal imageries. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2988453

Underwater sensing plays a crucial role in environmental protection and sustainable energy transitions, supporting marine ecosystem monitoring, resource management, and infrastructure development. However, visual perception in underwater environments... Read More about Advanced modelling and analysis for quality assessment and enhancement of underwater multimodal imageries..

Predicting student next-term performance in degree programs using AI-based approach: a case study from Ghana. (2025)
Journal Article
DIEKUU, J.-B., MEKALA, M.S., ABONIE, U.S., ISAACS, J. and ELYAN, E. 2025. Predicting student next-term performance in degree programs using AI-based approach: a case study from Ghana. Cogent education [online], 12(1), article number 2481000. Available from: https://doi.org/10.1080/2331186X.2025.2481000

Student performance can fluctuate over time due to various factors (e.g. previous assignment grades, social life and economic conditions). Temporal dynamics, such as semester-to-semester variations, and changes in students' academic achievements, beh... Read More about Predicting student next-term performance in degree programs using AI-based approach: a case study from Ghana..

Geothermal cooling solutions for rural communities at Homa Bay, Kenya: a CFD modelling study. (2025)
Journal Article
HOSSAIN, M., LASSALE, M., VERTIGANS, S., DEVECI, G., ELYAN, E., BURGESS, K. and OKOWA, M. 2025. Geothermal cooling solutions for rural communities at Homa Bay, Kenya: a CFD modelling study. International journal of sustainable engineering [online], 18(1), article number 2480095. Available from: https://doi.org/10.1080/19397038.2025.2480095

A Computational Fluid Dynamics (CFD) modelling study has been presented to analyse the cooling impact of a geothermal cooling system based on the Earth-Air-Tunnel-Heat-Exchanger (EATHE) concept to provide passive cooling to heat-stressed vulnerable p... Read More about Geothermal cooling solutions for rural communities at Homa Bay, Kenya: a CFD modelling study..

Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. (2024)
Journal Article
TOLIE, H.F., REN, J., CHEN, R., ZHAO, H. and ELYAN, E. 2025. Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain. Engineering applications of artificial intelligence [online], 141, article number 109730. Available from: https://doi.org/10.1016/j.engappai.2024.109730

In subsea environments, sound navigation and ranging (SONAR) images are widely used for exploring and monitoring infrastructures due to their robustness and insensitivity to low-light conditions. However, their quality can degrade during acquisition... Read More about Blind sonar image quality assessment via machine learning: leveraging micro- and macro-scale texture and contour features in the wavelet domain..

A novel ensemble aggregation method based on deep learning representation. (2024)
Presentation / Conference Contribution
NGUYEN, T.T., ELYAN, E., DANG, T., NGUYEN, T.T. and LONGMUIR, M. 2025. A novel ensemble aggregation method based on deep learning representation. In Antonacopoulos, A., Chaudhuri, S., Chellappa, R., et al. (eds.) Pattern recognition: proceedings of the 27th International conference on pattern recognition, 1-5 December 2024, Kolkata, India. Lecture notes in computer science, 15324. Cham: Springer [online], pages 31-46. Available from: https://doi.org/10.1007/978-3-031-78383-8_3

We propose a novel ensemble aggregation method by using a deep learning-based representation approach. Specifically, we applied the Cross-Validation procedure on training data with a number of learning algorithms to obtain the predictions for trainin... Read More about A novel ensemble aggregation method based on deep learning representation..

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)..

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

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

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

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

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

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

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

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

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

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

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

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

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..

All Outputs (109)