Outputs (1087)

Conceptual modelling of explanation experiences through the iSeeonto ontology. (2023)
Conference Proceeding
CARO-MARTÍNEZ, M., WIJEKOON, A., RECIO-GARCÍA, J.A., CORSAR, D. and NKISI-ORJI, I. 2022. Conceptual modelling of explanation experiences through the iSeeonto ontology. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 117-128. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_86.pdf

Explainable Artificial Intelligence is a big research field required in many situations where we need to understand Artificial Intelligence behaviour. However, each explanation need is unique which makes it difficult to apply explanation techniques a... Read More about Conceptual modelling of explanation experiences through the iSeeonto ontology..

Advanced persistent threats detection based on deep learning approach. (2023)
Conference Proceeding
EKE, H.N. and PETROVSKI, A. 2023. Advanced persistent threats detection based on deep learning approach. In Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber physical systems international conference 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China. Piscataway: IEEE [online], pages 1-10. Available from: https://doi.org/10.1109/ICPS58381.2023.10128062

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach netw... Read More about Advanced persistent threats detection based on deep learning approach..

iSee: intelligent sharing of explanation experiences. (2023)
Conference Proceeding
MARTIN, K., WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJI, I., CORSAR, D., DÍAZ-AGUDO, B., RECIO-GARCÍA, J.A., CARO-MARTÍNEZ, M., BRIDGE, D., PRADEEP, P., LIRET, A. and FLEISCH, B. 2022. iSee: intelligent sharing of explanation experiences. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 231-232. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdf

The right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of ex... Read More about iSee: intelligent sharing of explanation experiences..

Introducing Clood CBR: a cloud based CBR framework. (2023)
Conference Proceeding
PALIHAWADANA, C., NKISI-ORJI, I., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Introducing Clood CBR: a cloud based CBR framework. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 233-234. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_108.pdf

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of applications have been built using monolithi... Read More about Introducing Clood CBR: a cloud based CBR framework..

Explainable weather forecasts through an LSTM-CBR twin system. (2023)
Conference Proceeding
PIRIE, C., SURESH, M., SALIMI, P., PALIHAWADANA, C. and NANAYAKKARA, G. 2022. Explainable weather forecasts through an LSTM-CBR twin system. In Reuss, P. and Schönborn, J. (eds.) Workshop proceedings of the 30th International conference on case-based reasoning (ICCBR-WS 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3389. Aachen: CEUR-WS [online], pages 256-260. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_XCBR_Challenge_RGU.pdf

In this paper, we explore two methods for explaining LSTM-based temperature forecasts using previous 14 day progressions of humidity and pressure. First, we propose and evaluate an LSTM-CBR twin system that generates nearest-neighbors that can be vis... Read More about Explainable weather forecasts through an LSTM-CBR twin system..

Tensor singular spectral analysis for 3D feature extraction in hyperspectral images. (2023)
Journal Article
FU, H., SUN, G., ZHANG, A., SHAO, B., REN, J. and JIA, X. 2023. Tensor singular spectral analysis for 3D feature extraction in hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 61, article 5403914. Available from: https://doi.org/10.1109/TGRS.2023.3272669

Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral and spatial properties in three dimensions is challenging. Conventional spectral-spatial methods usually extract spectral and spatial information separately,... Read More about Tensor singular spectral analysis for 3D feature extraction in hyperspectral images..

H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification. (2023)
Journal Article
LIU, X., DONG, Z., LI, H., REN, J., ZHAO, H., LI, H., CHEN, W. and XIAO, Z. 2023. H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification. Remote sensing [online], 15(10), article 2497. Available from: https://doi.org/10.3390/rs15102497

Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this pa... Read More about H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification..

Ensemble common features technique for lightweight intrusion detection in industrial control system. (2023)
Conference Proceeding
OTOKWALA, U.J. and PETROVSKI, A. 2023. Ensemble common features technique for lightweight intrusion detection in industrial control system. In Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China. Piscataway: IEEE [online], 10128040. Available from: https://doi.org/10.1109/icps58381.2023.10128040

The integration of the Industrial Control System (ICS) with corporate intranets and the internet has exposed the previously isolated SCADA system to a wide range of cyber-attacks. Interestingly, the vulnerabilities in the Modbus protocol, with which... Read More about Ensemble common features technique for lightweight intrusion detection in industrial control system..

Bayesian optimized autoencoder for predictive maintenance of smart packaging machines. (2023)
Conference Proceeding
ARIFEEN, M. and PETROVSKI, A. 2023. Bayesian optimized autoencoder for predictive maintenance of smart packaging machines. In Proceedings of the 6th IEEE (Institute of Electrical and Electronics Engineers) International conference on Industrial cyber-physical systems 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China. Piscataway: IEEE [online], 10128064. Available from: https://doi.org/10.1109/icps58381.2023.10128064

Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can... Read More about Bayesian optimized autoencoder for predictive maintenance of smart packaging machines..

This changes to that: combining causal and non-causal explanations to generate disease progression in capsule endoscopy. (2023)
Conference Proceeding
VATS, A., MOHAMMED, A., PEDERSEN, M. and WIRATUNGA, N. 2023. This changes to that: combining causal and non-causal explanations to generate disease progression in capsule endoscopy. In Proceedings of the 2023 IEEE international conference on acoustics, speech and signal processing (ICASSP 2023), 4-10 June 2023, Rhodes Island, Greece. Piscataway: IEEE [online], paper number 1771. Available from: https://doi.org/10.1109/ICASSP49357.2023.10096931

The need to understand the decision-making mechanisms of deep learning networks has led to a growing effort in exploring both modal-dependent and model-agnostic research methods. Although both of these ideas provide transparency for automated decisio... Read More about This changes to that: combining causal and non-causal explanations to generate disease progression in capsule endoscopy..