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Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. (2023)
Conference Proceeding
COLLINS, J., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2023. Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. To be published in AI-XL: proceedings of the 43rd SGAI (Specialist Group on Artificial Intelligence) Artificial intelligence international conference 2023 (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, x. Cham: Springer [online], (forthcoming).

Rosters are often used for real-world staff scheduling requirements. Multiple design factors such as demand variability, shift type placement, annual leave requirements, staff well-being and the placement of trainees need to be considered when constr... Read More about Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement..

Modeling and simulation of heterojunction solar cell; determination of optimal values. (2023)
Conference Proceeding
LAWAL, S.M., FOUGH, N., SELLAMI, N. and MUHAMMAD-SUKKI, F. 2023. Modeling and simulation of heterojunction solar cell: determination of optimal values. In Proceedings of the 21st IEEE (Institute of Electrical and Electronics Engineers) Interregional NEWCAS conference 2023 (NEWCAS 2023), 26-28 June 2023, Edinburgh, UK. Piscataway: IEEE [online], article 10198061. Available from: https://doi.org/10.1109/NEWCAS57931.2023.10198061

A heterojunction solar cell of ZnSe/ZnO/CIGS/Si structure has been simulated in order to determine the optimal values. The performed modeling and Simulation is used to get an idea and identify the optimal values that can be use in the manufacturing p... Read More about Modeling and simulation of heterojunction solar cell; determination of optimal values..

Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data. (2023)
Conference Proceeding
STEWART, C., FUNG, WAI KEUNG, FOUGH, N. and PRABHU, R. 2023. Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data. In Proceedings of the 21st IEEE (Institute of Electrical and Electronics Engineers) Interregional NEWCAS conference 2023 (NEWCAS 2023), 26-28 June 2023, Edinburgh, UK. Piscataway: IEEE [online], article 10198101. Available from: https://doi.org/10.1109/NEWCAS57931.2023.10198101

Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Se... Read More about Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data..

Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. (2023)
Conference Proceeding
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2023. Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. In GECCO’23 companion: proceedings of the 2023 Genetic and evolutionary computation conference companion, 15-19 July 2023, Lisbon, Portugal. New York: ACM [online], pages 1648-1656. Available from: https://doi.org/10.1145/3583133.3596353

In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of solution quali... Read More about Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis..

The current and future role of visual question answering in eXplainable artificial intelligence. (2023)
Conference Proceeding
CARO-MARTINEZ, M., WIJEKOON, A., DIAZ-AGUDO, B. and RECIO-GARCIA, J.A. 2023. The current and future role of visual question answering in eXplainable artificial intelligence. In Malburg, L. and Verma, D. (eds.) ICCBR-WS 2023: proceedings of the workshops at the 31st International conference on case-based reasoning workshop (ICCBR-WS 2023), co-located with the 31st International conference on case-based reasoning (ICCBR 2023), 17 July 2023, Aberdeen, UK. Aachen: CEUR workshop proceedings [online], pages 172-183. Available from: https://ceur-ws.org/Vol-3438/paper_13.pdf

Over the last few years, we have seen how the interest of the computer science research community on eXplainable Artificial Intelligence has grown in leaps and bounds. The reason behind this rise is the use of Artificial Intelligence in many daily li... Read More about The current and future role of visual question answering in eXplainable artificial intelligence..

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.) ICCBR-WS 2023: proceedings of the workshops at the 31st International conference on case-based reasoning workshop (ICCBR-WS 2023), co-located with the 31st International conference on case-based reasoning (ICCBR 2023), 17 July 2023, Aberdeen, UK. Aachen: CEUR workshop proceedings [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..

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. To be presented at the 26th European conference on artificial intelligence 2023 (ECAI-2023), 30 September - 5 October 2023, Kraków, Poland.

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

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. (2023)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. In De Capitani di Vimercati, S. and Samarati, P. (eds.) Proceedings of the 20th International conference on security and cryptography, 10-12 July 2023, Rome, Italy, volume 1. Setúbal: SciTePress [online], pages 659-666. Available from: https://doi.org/10.5220/0012060400003555

Ensuring the security of Android applications is a vital and intricate aspect requiring careful consideration during development. Unfortunately, many apps are published without sufficient security measures, possibly due to a lack of early vulnerabili... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models..

Android Code Vulnerabilities Early Detection using AI-Powered ACVED plugin. (2023)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android Code Vulnerabilities Early Detection using AI-Powered ACVED plugin. In Atluri, V. and Ferrara, A.L. (eds.) Data and applications security and privacy XXXVII; proceedings of the 37th annual IFIP WG (International Federation for Information Processing Working Group) 11.3 Data and applications security and privacy 2023 (DBSec 2023), 19-21 July 2023, Sophia-Antipolis, France. Lecture notes in computer science (LNCS), 13942. Cham: Springer [online], pages 339-357. Available from: https://doi.org/10.1007/978-3-031-37586-6_20

During Android application development, ensuring adequate security is a crucial and intricate aspect. However, many applications are released without adequate security measures due to the lack of vulnerability identification and code verification at... Read More about Android Code Vulnerabilities Early Detection using AI-Powered ACVED plugin..

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..

Unmasking the imposters: task-specific feature learning for face presentation attack detection. (2023)
Conference Proceeding
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2023. Unmasking the imposters: task-specific feature learning for face presentation attack detection. In Proceedings of the 2023 International joint conference on neural networks (IJCNN2023), 18-23 June 2023, Gold Coast, Australia. Piscataway: IEEE [online], 10191953. Available from: https://doi.org/10.1109/IJCNN54540.2023.10191953

Presentation attacks pose a threat to the reliability of face recognition systems. A photograph, a video, or a mask representing an authorised user can be used to circumvent the face recognition system. Recent research has demonstrated high accuracy... Read More about Unmasking the imposters: task-specific feature learning for face presentation attack detection..

Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. (2023)
Conference Proceeding
TORAL-QUIJAS, L.A., ELYAN, E., MORENO-GARCÍA, C.F. and STANDER, J. 2023. Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections. In Iliadis, L, Maglogiannis, I., Alonso, S., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 24th International conference on engineering applications of neural networks (EAAAI/EANN 2023), 14-17 June 2023, León, Spain. Communications in computer and information science, 1826. Cham: Springer [online], pages 217-226. Available from: https://doi.org/10.1007/978-3-031-34204-2_19

Inspecting circumferential welds in caissons is a critical task for ensuring the safety and reliability of structures in the offshore industry. However, identifying and classifying different types of circumferential welds can be challenging in subsea... Read More about Digital transformation for offshore assets: a deep learning framework for weld classification in remote visual inspections..

Towards autonomous developmental artificial intelligence: case study for explainable AI. (2023)
Conference Proceeding
STARKEY, A. and EZENKWU, C.P. 2023. Towards autonomous developmental artificial intelligence: case study for explainable AI. In Maglogiannis, I., Iliadis, L., MacIntyre, J. and Dominguez, M. (eds.) Artificial intelligence applications and innovations: proceedings of the 19th IFIP (International Federation for Information Processing) WG 12.5 Artificial intelligence applications and innovations international conference (AIAI 2023), 14-17 June 2023, León, Spain. IFIP advances in information and communication technology, 676. Cham: Springer [online], pages 94-105. Available from: https://doi.org/10.1007/978-3-031-34107-6_8

State-of-the-art autonomous AI algorithms such as reinforcement learning and deep learning techniques suffer from high computational complexity, poor explainability ability, and a limited capacity for incremental adaptive learning. In response to the... Read More about Towards autonomous developmental artificial intelligence: case study for explainable AI..

Towards expert systems for improved customer services using ChatGPT as an inference engine. (2023)
Conference Proceeding
EZENKWU, C.P. 2023. Towards expert systems for improved customer services using ChatGPT as an inference engine. To be presented at the 2023 IEEE (Institute of electrical and Electronics Engineers) International conference on digital applications, transformation and economy (ICDATE 2023), 14-16 July 2023, Miri, Malaysia, (accepted).

By harnessing both implicit and explicit customer data, companies can develop a more comprehensive understanding of their consumers, leading to better customer engagement and experience, and improved loyalty. As a result, businesses have embraced man... Read More about Towards expert systems for improved customer services using ChatGPT as an inference engine..

A machine learning-based job forecasting and trend analysis system to predict future job markets using historical data. (2023)
Conference Proceeding
SENTHURVELAUTHAM, S. and SENANAYAKE, N. 2023. A machine learning-based job forecasting and trend analysis system to predict future job markets using historical data. In Proceedings of the 8th IEEE (Institute of Electrical and Electronics Engineers) International conference for convergence in technology 2023 (I2CT 2023), 7-9 April 2023, Lonavla, India. Piscataway: IEEE [online], 10126233. Available from: https://doi.org/10.1109/I2CT57861.2023.10126233

Over the last two decades, technological advancements have created more job markets and job opportunities than ever. With the ever-increasing demand, it has become vital for academic institutions and businesses to keep up with employment requirements... Read More about A machine learning-based job forecasting and trend analysis system to predict future job markets using historical data..

A green AI model selection strategy for computer-aided mpox detection. (2023)
Conference Proceeding
EZENKWU, C.P., STEPHEN, B.U.-A., AFFIAH, I. and DANIEL, B. [2023]. A green AI model selection strategy for computer-aided mpox detection. In Proceedings of the 16th IEEE Africon conference (IEEE AFRICON 2023): advancing technology in Africa towards presence on the global stage, 20-22 September 2023, Nairobi, Kenya. Piscataway: IEEE [online], (accepted).

With the recent global surge in mpox (formerly monkeypox) cases, researchers have proposed deep learning technologies for early detection of the disease from skin lesion images. However, many of these researchers follow the current Red AI trend of se... Read More about A green AI model selection strategy for computer-aided mpox detection..

AI-powered vulnerability detection for secure source code development. (2023)
Conference Proceeding
RAJAPAKSHA, S., SENANAYAKE, J., KALUTARAGE, H. and AL-KADRI, M.O. 2023. AI-powered vulnerability detection for secure source code development. In Bella, G., Doinea, M. and Janicke, H. (eds.) Innovative security solutions for information technology and communications: revised selected papers of the 15th International conference on Security for information technology and communications 2022 (SecITC 2022), 8-9 December 2022, [virtual conference]. Lecture notes in computer sciences, 13809. Cham: Springer [online], pages 275-288. Available from: https://doi.org/10.1007/978-3-031-32636-3_16

Vulnerable source code in software applications is causing paramount reliability and security issues. Software security principles should be integrated to reduce these issues at the early stages of the development lifecycle. Artificial Intelligence (... Read More about AI-powered vulnerability detection for secure source code development..

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.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, 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..

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.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, 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..

iSee: intelligent sharing of explanation experiences. (2023)
Conference Proceeding
MARTIN, K., WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJIC, 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.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, 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..