Outputs (98)

Mining potentially explanatory patterns via partial solutions. (2024)
Presentation / Conference Contribution
CATALANO, G.A.P.I., BROWNLEE, A.E.I., CAIRNS, D., MCCALL, J. and AINSLIE, R. 2024. Mining potentially explanatory patterns via partial solutions. In GECCO'24 companion: proceedings of the 2024 Genetic and evolutionary computation conference companion 2024 (GECCO'24 companion), 14-18 July 2024, Melbourne, Australia. New York: ACM [online], pages 567-570. Available from: https://doi.org/10.1145/3638530.3654318

We introduce Partial Solutions to improve the explainability of genetic algorithms for combinatorial optimization. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but al... Read More about Mining potentially explanatory patterns via partial solutions..

A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. (2024)
Presentation / Conference Contribution
DANG, T., NGUYEN, T.T., MCCALL, J., HAN, K. and LIEW, A.W.-C. 2024. A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2024), 30 June - 05 July 2024, Yokohama, Japan. Available from: https://doi.org/10.1109/CEC60901.2024.10611960

Deep neural networks (DNN) has achieved great successes across multiple domains. In recent years, a number of approaches have emerged on automatically finding the optimal DNN configurations. A technique among these approaches which show great promise... Read More about A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture..

Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset. (2024)
Presentation / Conference Contribution
YAN, Y., LI, Y., LIN, H., SARKER, M.M.K., REN, J. and MCCALL, J. 2024. Underwater object detection for smooth and autonomous operations of naval missions: a pilot dataset. In Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 113-122. Available from: https://doi.org/10.1007/978-981-97-1417-9_11

Underwater object detection is essential for ensuring autonomous naval operations. However, this task is challenging due to the complexities of underwater environments that often degrade image quality, thereby hampering the performance of detection a... Read More about Underwater object detection for smooth and autonomous operations of naval missions: a pilot Dataset..

Two-layer ensemble of deep learning models for medical image segmentation. (2024)
Journal Article
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2024. Two-layer ensemble of deep learning models for medical image segmentation. Cognitive computation [online], 16(3), pages 1141-1160. Available from: https://doi.org/10.1007/s12559-024-10257-5

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation al... Read More about Two-layer ensemble of deep learning models for medical image segmentation..

A weighted ensemble of regression methods for gross error identification problem. (2023)
Presentation / Conference Contribution
DOBOS, D., DANG, T., NGUYEN, T.T., MCCALL, J., WILSON, A., CORBETT, H. and STOCKTON, P. 2023. A weighted ensemble of regression methods for gross error identification problem. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Symposium series on computational intelligence (SSCI 2023), 5-8 December 2023, Mexico City, Mexico. Piscataway: IEEE [online], pages 413-420. Available from: https://doi.org/10.1109/SSCI52147.2023.10371882

In this study, we proposed a new ensemble method to predict the magnitude of gross errors (GEs) on measurement data obtained from the hydrocarbon and stream processing industries. Our proposed model consists of an ensemble of regressors (EoR) obtaine... Read More about A weighted ensemble of regression methods for gross error identification problem..

Explaining a staff rostering problem by mining trajectory variance structures. (2023)
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., ZĂVOIANU, A.-C., BROWNLEE, A.E.I. and AINSLIE, R. 2023. Explaining a staff rostering problem by mining trajectory variance structures. 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 275-290. Available from: https://doi.org/10.1007/978-3-031-47994-6_27

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behav... Read More about Explaining a staff rostering problem by mining trajectory variance structures..

Comparison of simulated annealing and evolution strategies for optimising cyclical rosters with uneven demand and flexible trainee placement. (2023)
Presentation / Conference Contribution
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. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence 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, 14381. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-47994-6_39

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..

Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. (2023)
Presentation / Conference Contribution
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..

DEFEG: deep ensemble with weighted feature generation. (2023)
Journal Article
LUONG, A.V., NGUYEN, T.T., HAN, K., VU, T.H., MCCALL, J. and LIEW, A.W.-C. 2023. DEFEG: deep ensemble with weighted feature generation. Knowledge-based systems [online], 275, article 110691. Available from: https://doi.org/10.1016/j.knosys.2023.110691

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcFores... Read More about DEFEG: deep ensemble with weighted feature generation..

On the elusivity of dynamic optimisation problems. (2023)
Journal Article
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2023. On the elusivity of dynamic optimisation problems. Swarm and evolutionary computation [online], 78, article 101289. Available from: https://doi.org/10.1016/j.swevo.2023.101289

The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a bet... Read More about On the elusivity of dynamic optimisation problems..