All Outputs (114)

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..

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..

Evaluating the transferability of personalised exercise recognition models. (2020)
Conference Proceeding
WIJEKOON, A. and WIRATUNGA, N. 2020. Evaluating the transferability of personalised exercise recognition models. 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 32-44. Available from: https://doi.org/10.1007/978-3-030-48791-1_3

Exercise Recognition (ExR) is relevant in many high impact domains, from health care to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typica... Read More about Evaluating the transferability of personalised exercise recognition models..

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..

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..

DPIA in context: applying DPIA to assess privacy risks of cyber physical systems. (2020)
Journal Article
HENRIKSEN-BULMER, J., FAILY, S. and JEARY, S. 2020. DPIA in context: applying DPIA to assess privacy risks of cyber physical systems. Future internet [online], 12(5), article 93. Available from: https://doi.org/10.3390/fi12050093

Cyber Physical Systems (CPS) seamlessly integrate physical objects with technology, thereby blurring the boundaries between the physical and virtual environments. While this brings many opportunities for progress, it also adds a new layer of complexi... Read More about DPIA in context: applying DPIA to assess privacy risks of cyber physical systems..

Evolving interval-based representation for multiple classifier fusion. (2020)
Journal Article
NGUYEN, T.T., DANG,M.T., BAGHEL, V.A., LUONG, A.V., MCCALL, J. and LIEW, A.W.-C. 2020 Evolving interval-based representation for multiple classifier fusion. Knowledge-based systems [online], 201-202, article ID 106034. Available from: https://doi.org/10.1016/j.knosys.2020.106034

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation... Read More about Evolving interval-based representation for multiple classifier fusion..

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. (2020)
Journal Article
FU, H., SUN, G., ZABALZA, J., ZHANG, A., REN, J. and JIA, X. 2020. A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 2214-2225. Available from: https://doi.org/10.1109/JSTARS.2020.2992230

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conve... Read More about A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging..

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 to recognise exercises for the self-management of low back pain. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., COOPER, K. and BACH, K. 2020. Learning to recognise exercises for the self-management of low back pain. In Barták, R. and Bell, E. (eds.). Proceedings of the 33rd International Florida Artificial Intelligence Research Society (FLAIRS) 2020 conference (FLAIRS-33), 17-20 May 2020, Miami Beach, USA. Palo Alto: AAAI Press [online], pages 347-352. Available from: https://aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18460

Globally, Low back pain (LBP) is one of the top three contributors to years lived with disability. Self-management with an active lifestyle is the cornerstone for preventing and managing LBP. Digital interventions are introduced in the recent past to... Read More about Learning to recognise exercises for the self-management of low back pain..

Content-sensitive superpixel generation with boundary adjustment. (2020)
Journal Article
ZHANG, D., XIE, G., REN, J., ZHANG, Z., BAO, W. and XU, X. 2020. Content-sensitive superpixel generation with boundary adjustment. Applied sciences [online], 10(9), article 3150. Available from: https://doi.org/10.3390/app10093150

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy... Read More about Content-sensitive superpixel generation with boundary adjustment..

Spatial residual blocks combined parallel network for hyperspectral image classification. (2020)
Journal Article
ZHANG, B., QING, C., XU, X. and REN, J. 2020. Spatial residual blocks combined parallel network for hyperspectral image classification. IEEE access [online], 8, pages 74513-74524. Available from: https://doi.org/10.1109/ACCESS.2020.2988553

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI cla... Read More about Spatial residual blocks combined parallel network for hyperspectral image classification..

Privacy, security, legal and technology acceptance elicited and consolidated requirements for a GDPR compliance platform (2020)
Journal Article
TSOHOU, A., MAGKOS, E., MOURATIDIS, H., CHRYSOLORAS, G., PIRAS, L., PAVLIDIS, M., DEBUSSCHE, J., ROTOLONI, M. and CRESPO, B. G.-N. 2020. Privacy, security, legal and technology acceptance elicited and consolidated requirements for a GDPR compliance platform. Information and computer security [online], 28(4), pages 531-553. Available from: https://doi.org/10.1108/ICS-01-2020-0002

Purpose– General data protection regulation (GDPR) entered into force in May 2018 for enhancing personal data protection. Even though GDPR leads toward many advantages for the data subjects it turned out to be a significant challenge. Organizations n... Read More about Privacy, security, legal and technology acceptance elicited and consolidated requirements for a GDPR compliance platform.

On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. (2020)
Conference Proceeding
ZAVOIANU, A.-C., KITZBERGER, M., BRAMERDORFER, G. and SAMINGER-PLATZ, S. 2020. On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: EUROCAST 2019: revised selected papers from the proceedings of the 17th International conference on computer aided systems theory (EUROCAST 2019), 17-22 February 2019, Las Palmas de Gran Canaria, Spain. Lecture notes in computer science, 12013. Cham: Springer [online], part 1, pages 319-326. Available from: https://doi.org/10.1007/978-3-030-45093-9_39

We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped param... Read More about On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks..

A simple encoder scheme for distributed residual video coding. (2020)
Journal Article
HU, C., ZHAO, Y., YU, L., JIANG, Y. and XIONG, Y. 2020. A simple encoder scheme for distributed residual video coding. Multimedia tools and applications [online], 79(27-28), pages 20061-20078. Available from: https://doi.org/10.1007/s11042-020-08811-y

Rate-Distortion (RD) performance of Distributed Video Coding (DVC) is considerably less than that of conventional predictive video coding. In order to reduce the performance gap, many methods and techniques have been proposed to improve the coding ef... Read More about A simple encoder scheme for distributed residual video coding..

Made-up rubbish: design fiction as a tool for participatory Internet of Things research. (2020)
Journal Article
JACOBS, N., MARKOVIC, M., COTTRILL, C.D., EDWARDS, P., CORSAR, D. and SALT, K. 2020. Made-up rubbish, design fiction as a tool for participatory Internet of Things research. Design journal [online], 23(3), pages 419-440. Available from: https://doi.org/10.1080/14606925.2020.1744259

As Internet of Things (IoT) technologies become embedded in public infrastructure, it is important that we consider how they may introduce new challenges in areas such as privacy and governance. Public technology implementations can be more democrati... Read More about Made-up rubbish: design fiction as a tool for participatory Internet of Things research..

Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. (2020)
Journal Article
SUN, H., REN, J., ZHAO, H., SUN, G., LIAO, W., FANG, Z. and ZABALZA, J. 2022. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE transactions on cybernetics [online], 52(1), pages 215-227. Available from: https://doi.org/10.1109/TCYB.2020.2977750

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still la... Read More about Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection..

Urban PM2.5 concentration prediction via attention-based CNN–LSTM. (2020)
Journal Article
LI, S., XIE, G., REN, J., GUO, L., YANG, Y. and XU, X. 2020. Urban PM2.5 concentration prediction via attention-based CNN–LSTM. Applied sciences [online], 10(6), article 1953. Available from: https://doi.org/10.3390/app10061953

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the eff... Read More about Urban PM2.5 concentration prediction via attention-based CNN–LSTM..

Exemplar-supported representation for effective class-incremental learning. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Exemplar-supported representation for effective class-incremental learning. IEEE access [online], 8, pages 51276-51284. Available from: https://doi.org/10.1109/ACCESS.2020.2980386

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new... Read More about Exemplar-supported representation for effective class-incremental learning..