Outputs (100)

Clinical dialogue transcription error correction with self-supervision. (2023)
Conference Proceeding
NANAYAKKARA, G., WIRATUNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2023. Clinical dialogue transcription error correction with self-supervision. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 33-46. Available from: https://doi.org/10.1007/978-3-031-47994-6_3

A clinical dialogue is a conversation between a clinician and a patient to share medical information, which is critical in clinical decision-making. The reliance on manual note-taking is highly inefficient and leads to transcription errors when digit... Read More about Clinical dialogue transcription error correction with self-supervision..

Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. (2023)
Conference Proceeding
SALIMI, P., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2023. Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method. In Gal, K., Nowé, A., Nalepa, G.J., Fairstein, R. and Rădulescu, R. (eds.) ECAI 2023: proceedings of the 26th European conference on artificial intelligence (ECAI 2023), 30 September - 4 October 2023, Kraków, Poland. Frontiers in artificial intelligence and applications, 372. Amsterdam: IOS Press [online], pages 2057-2064. Available from: https://doi.org/10.3233/FAIA230499

Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. However, many cf-XAI methods neglect the feasibility of those changes. In this paper, we introduce a novel... Read More about Towards feasible counterfactual explanations: a taxonomy guided template-based NLG method..

Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023) (2023)
Conference Proceeding
IBRAHIM, Z., WU, H. and WIRATUNGA, N. (eds.) 2023. Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023), co-located with the 32nd International joint conference on artificial intelligence (IJCAI 2023), 20 August 2023, Macao, China. CEUR workshop proceedings, 3479. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-3479/

This workshop is centred around novel AI methodologies that aim to solve some of the grand challenges associated with medical data. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI 2023), this year's works... Read More about Proceedings of the 6th International workshop on knowledge discovery from healthcare data (KDH@IJCAI 2023).

Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. (2023)
Journal Article
OFORI-BOATENG, R., ACEVES-MARTINS, M., JAYNE, C., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2023. Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. Porcedia computer science [online], 222: selected papers from the 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023), co-located with the 2023 International joint conference on neural networks (IJCNN), 18-32 June 2023, Gold Coast, Australia, pages 114-126. Available from: https://doi.org/10.1016/j.procs.2023.08.149

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screenin... Read More about Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation..

CBR driven interactive explainable AI. (2023)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., MARTIN, K., CORSAR, D., NKISI-ORJI, I., PALIHAWADANA, C., BRIDGE, D., PRADEEP, P., AGUDO, B.D. and CARO-MARTÍNEZ, M. 2023. CBR driven interactive explainable AI. In MASSIE, S. and CHAKRABORTI, S. (eds.) 2023. Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023, (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages169-184. Available from: https://doi.org/10.1007/978-3-031-40177-0_11

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requi... Read More about CBR driven interactive explainable AI..

Failure-driven transformational case reuse of explanation strategies in CloodCBR. (2023)
Conference Proceeding
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and CORSAR, D. 2023. Failure-driven transformational case reuse of explanation strategies in CloodCBR. In Massie, S. and Chakraborti, S. (eds.) Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023 (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages 279-293. Available from: https://doi.org/10.1007/978-3-031-40177-0_18

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves trans... Read More about Failure-driven transformational case reuse of explanation strategies in CloodCBR..

A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction. (2023)
Journal Article
WIJEKOON, A. and WIRATUNGA, N. 2023. A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction. Knowledge-based systems [online], 278, article 110830. Available from: https://doi.org/10.1016/j.knosys.2023.110830

Counterfactual explanations highlight actionable knowledge which helps to understand how a machine learning model outcome could be altered to a more favourable outcome. Understanding actionable corrections in source code analysis can be critical to p... Read More about A user-centred evaluation of DisCERN: discovering counterfactuals for code vulnerability detection and correction..

AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. (2023)
Conference Proceeding
PIRIE, C., WIRATUNGA, N., WIJEKOON, A. and MORENO-GARCIA, C.F. 2023. AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics. In Malburg, L. and Verma, D. (eds.) Workshop proceedings of the 31st International conference on case-based reasoning (ICCBR-WS 2023), 17 July 2023, Aberdeen, UK. CEUR workshop proceedings, 3438. Aachen: CEUR-WS [online], pages 184-199. Available from: https://ceur-ws.org/Vol-3438/paper_14.pdf

As deep learning models become increasingly complex, practitioners are relying more on post hoc explanation methods to understand the decisions of black-box learners. However, there is growing concern about the reliability of feature attribution expl... Read More about AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics..

Machine learning for risk stratification of diabetic foot ulcers using biomarkers. (2023)
Conference Proceeding
MARTIN, K., UPHADYAY, A., WIJEKOON, A., WIRATUNGA, N. and MASSIE, S. [2023]. Machine learning for risk stratification of diabetic foot ulcers using biomarkers. To be presented at the 2023 International conference on computational science (ICCS 2023): computing at the cutting edge of science, 3-5 July 2023, Prague, Czech Republic: [virtual event].

Development of a Diabetic Foot Ulcer (DFU) causes a sharp decline in a patient's health and quality of life. The process of risk stratification is crucial for informing the care that a patient should receive to help manage their Diabetes before an ul... Read More about Machine learning for risk stratification of diabetic foot ulcers using biomarkers..

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..