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A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. (2022)
Thesis
HAJAR, M.S. 2022. A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987863

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraint... Read More about A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks..

Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. (2022)
Thesis
ZAKARIYYA, I. 2022. Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1987917

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection... Read More about Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring..

Composite repair and remanufacturing. (2022)
Book Chapter
VON FREEDEN, J., DE WIT, J., CABA, S., KROLL, S., ZHAO, H., REN, J., YAN, Y., ARSHED, F., AHMAD, A. and XIROUCHAKIS, P. 2022. Composite repair and remanufacturing. In Colledani, M. and Turri, S. (eds.) Systemic circular economy solutions for fiber reinforced composites. Cham: Springer [online], pages 191-214. Available from: https://doi.org/10.1007/978-3-031-22352-5_10

For the reuse of components and structures made of fiber composite materials, a complete remanufacturing process chain is necessary to prepare the parts for a further life cycle. The first step is to dismantle the parts to be reused. Fiber composite... Read More about Composite repair and remanufacturing..

Piloting the learning by developing action model pedagogy in Finland HEIs. (2022)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2022. Piloting the learning by developing action model pedagogy in Finland HEIs. In Chova, L.G., Martínez, A.L. and Lees, J. (eds.) Proceedings of the 15th Annual international conference of education, research and innovation (ICERI2022), 7-9 November 2022, Seville, Spain. Valenca: IATED [online], pages 1856-1865. Available from: https://doi.org/10.21125/iceri.2022.0474

This article describes a study at Haaga-Helia University of Applied Sciences (Haaga-Helia) that aims to understand how suitable the Learning by Developing (LbD) action model is as a teaching and learning method for computing students. The research al... Read More about Piloting the learning by developing action model pedagogy in Finland HEIs..

Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation. (2022)
Conference Proceeding
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2022. Lightweight Interpolation-based surrogate modelling for multiobjective continuous optimisation. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 53-60. Available from: https://doi.org/10.1007/978-3-031-25312-6_6

We propose two surrogate-based strategies for increasing the convergence speed of multi-objective evolutionary algorithms (MOEAs) by stimulating the creation of high-quality individuals early in the run. Both offspring generation strategies are desig... Read More about Lightweight interpolation-based surrogate modelling for multi-objective continuous optimisation..

On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. (2022)
Conference Proceeding
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J. 2022. On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 104-111. Available from: https://doi.org/10.1007/978-3-031-25312-6_12

While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport sy... Read More about On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems..

Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. (2022)
Conference Proceeding
RICA, E., ALVAREZ, S., MORENO-GARCIA, C.F. and SERRATOSA, F. 2022. Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search. In Krzyzak, A., Suen, C.Y., Torsello, A. and Nobile, N. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2022 Joint International Association for Pattern Recognition (IAPR) international workshops on statistical techniques in pattern recognition, and structural and syntactic pattern recognition (S+SSPR 2022), 26-27 August 2022, Montréal, Canada. Lecture notes in computer science, 13813. Cham: Springer [online], pages 274-282. Available from: https://doi.org/10.1007/978-3-031-23028-8_28

Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final end the regeneration of the original com... Read More about Zero-error digitisation and contextualisation of piping and instrumentation diagrams using node classification and sub-graph search..

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..

The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape. (2022)
Conference Proceeding
SIEGEL, A.A., ZARB, M., ANDERSON, E., CRANE, B., GAO, A., LATULIPE, C., LOVELLETTE, E., MCNEILL, F. and MEHARG, D. 2022. The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape. In ITiCSE-WGR'22: proceedings of the 2022 Working group reports (WGR), co-located with the 27th Innovation and technology in computer science education annual conference (ITiCSE-WGR '22), 11-13 July 2022, Dublin, Ireland. New York: ACM [online], pages 165-190. Available from: https://doi.org/10.1145/3571785.3574126

Students have experienced incredible shifts in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these... Read More about The impact of COVID-19 on the CS student learning experience: how the pandemic has shaped the educational landscape..

An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification. (2022)
Journal Article
ZHAO, C., QIN, B., FENG, S., ZHU, W., ZHANG, L. and REN, J. 2022. An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 60, article 5546216. Available from: https://doi.org/10.1109/TGRS.2022.3230378

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performa... Read More about An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification..

Mobile Platform for livestock monitoring and inspection. (2022)
Conference Proceeding
FABIYI, S.D., REN, J., HAN, Y., ZHU, Q. and BARCLAY, D. 2022. Mobile platform for livestock monitoring and inspection. In Proceedings of the 3rd International informatics and software engineering conference 2022 (IISEC 2022), 15-16 December 2022, Ankara, Turkey. Piscataway: IEEE [online], article 9998279. Available from: https://doi.org/10.1109/iisec56263.2022.9998297

Livestock keepers acquire and manage information (e.g. identification numbers, images, etc.) about livestock to identify and keep track of livestock using systems with capabilities to extract such information. Examples of such systems are Radio Frequ... Read More about Mobile Platform for livestock monitoring and inspection..

GEMv2: multilingual NLG benchmarking in a single line of code. (2022)
Conference Proceeding
GEHRMANN, S., BHATTACHARJEE, A., MAHENDIRAN, A., WANG, A., PAPANGELIS, A., MADAAN, A., MCMILLAN-MAJOR, A., SHVETS, A., UPADHYAY, A. and BOHNET, B. 2022. GEMv2: multilingual NLG benchmarking in a single line of code. In Proceedings of the 2022 Conference on empirical methods in natural language processing: system demonstrations, 7-11 December 2022, Abu Dhabi, UAE. Stroudsburg: Association for Computational Linguistics [online], pages 266-281. Available from: https://aclanthology.org/2022.emnlp-demos.27/

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluati... Read More about GEMv2: multilingual NLG benchmarking in a single line of code..

Resource efficient federated deep learning for IoT security monitoring. (2022)
Conference Proceeding
ZAKARIYYA, I., KALUTARAGE, H. and AL-KADRI, M.O. 2022. Resource efficient federated deep learning for IoT security monitoring. In Li, W., Furnell, S. and Meng, W. (eds.) Attacks and defenses for the Internet-of-Things: revised selected papers from the 5th International workshop on Attacks and defenses for Internet-of-Things 2022 (ADIoT 2022), in conjunction with 27th European symposium on research in computer security 2022 (ESORICS 2022) 29-30 Septempber 2022, Copenhagen, Denmark. Lecture notes in computer science (LNCS), 13745. Cham: Springer [online], pages 122-142. Available from: https://doi.org/10.1007/978-3-031-21311-3_6

Federated Learning (FL) uses a distributed Machine Learning (ML) concept to build a global model using multiple local models trained on distributed edge devices. A disadvantage of the FL paradigm is the requirement of many communication rounds before... Read More about Resource efficient federated deep learning for IoT security monitoring..

Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation. (2022)
Conference Proceeding
DANG, T., NGUYEN, T.T., MCCALL, J. and LIEW, A.W.-C. 2022. Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation. In Ishibuchi, H., Kwoh, C.-K., Tan, A.-H., Srinivasan, D., Miao, C., Trivedi, A. and Crockett, K. (eds.) Proceedings of the 2022 IEEE Symposium series on computational intelligence (SSCI 2022), 4-7 December 2022, Singapore. Piscataway: IEEE [online], pages 269-276. Available from: https://doi.org/10.1109/SSCI51031.2022.10022114

Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notable successe... Read More about Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation..

Job assignment problem and traveling salesman problem: a linked optimisation problem. (2022)
Conference Proceeding
OGUNSEMI, A., MCCALL, J., KERN, M., LACROIX, B., CORSAR, D. and OWUSU, G. 2022. Job assignment problem and traveling salesman problem: a linked optimisation problem. In Bramer, M. and Stahl, F (eds.) Artificial intelligence XXXIX: proceedings of the 42nd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2022 (AI 2022), 13-15 December 2022, Cambridge, UK. Lecture notes in computer science (LNCS), 13652. Cham: Springer [online], pages 19-33. Available from: https://doi.org/10.1007/978-3-031-21441-7_2

Linked decision-making in service management systems has attracted strong adoption of optimisation algorithms. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems. This paper, theref... Read More about Job assignment problem and traveling salesman problem: a linked optimisation problem..

A robust exploration strategy in reinforcement learning based on temporal difference error. (2022)
Conference Proceeding
HAJAR, M.S., KALUTARAGE, H. and AL-KADRI, M.O. 2022. A robust exploration strategy in reinforcement learning based on temporal difference error. In Aziz, H., Corrêa, D. and French, T. (eds.) AI 2022: advances in artificial intelligence; proceedings of the 35th Australasian joint conference 2022 (AI 2022), 5-8 December 2022, Perth, Australia. Lecture notes in computer science (LNCS), 13728. Cham: Springer [online], pages 789-799. Available from: https://doi.org/10.1007/978-3-031-22695-3_55

Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation trade-off is still a fundamental dilemma in reinforcement learning. The learning agent needs to learn how to deal with a stochastic environment in orde... Read More about A robust exploration strategy in reinforcement learning based on temporal difference error..

Piloting the learning by development action model pedagogy in UK HEIs. (2022)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2022. Piloting the learning by development action model pedagogy in UK HEIs. In Proceedings of the 2022 Frontiers in education conference (FIE 2022): grand challenges in engineering education, 8-11 October 2022, Uppsala, Sweden. Piscataway: IEEE [online]. Available from: https://doi.org/10.1109/FIE56618.2022.9962469

This Research to Practice full paper presents pilot implementations of the Learning by Developing (LbD) at a higher educational institution in the UK as part of a project-based module. The study analyses the students' experiences of LbD and the perce... Read More about Piloting the learning by development action model pedagogy in UK HEIs..

Clinical dialogue transcription error correction using Seq2Seq models. (2022)
Conference Proceeding
NANAYAKKARA, G., WIRATURNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. In Shaban-Nejad, A., Michalowski, M. and Bianco, S. (eds.) Multimodal AI in healthcare: a paradigm shift in health intelligence; selected papers from the 6th International workshop on health intelligence (W3PHIAI-22), co-located with the 34th AAAI (Association for the Advancement of Artificial Intelligence) Innovative applications of artificial intelligence (IAAI-22), 28 February - 1 March 2022, [virtual event]. Studies in computational intelligence, 1060. Cham: Springer [online], pages 41-57. Available from: https://doi.org/10.1007/978-3-031-14771-5_4

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decis... Read More about Clinical dialogue transcription error correction using Seq2Seq models..

Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures. (2022)
Journal Article
ZABALZA, J., MURRAY, P., MARSHALL, S., REN, J., BERNARD, R. and HEPWORTH, S. 2023. Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures. IEEE sensors journal [online], 23(1), pages 452-459. Available from: https://doi.org/10.1109/JSEN.2022.3221680

Traditionally, Special Nuclear Material (SNM) at Sellafield has been stored in multi-layered packages, consisting of metallic cans and an over-layer of plasticized Polyvinyl Chloride (PVC) as an intermediate layer when transitioning between areas of... Read More about Hyperspectral imaging based detection of PVC during Sellafield repackaging procedures..

Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture. (2022)
Journal Article
HUANG, H., TANG, Y., TAN, Z., ZHUANG, J., HOU, C., CHEN, W. and REN, J. 2022. Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture. IEEE transactions on geoscience and remote sensing [online], 60, article number 4416013. Available from: https://doi.org/10.1109/TGRS.2022.3224580

Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses... Read More about Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture..