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Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation. (2022)
Presentation / Conference Contribution
ADEGBOYE, M.A., KARNIK, A., FUNG, W.-K. and PRABHU, R. 2022. Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation. In Proceedings of the 9th International conference on soft computing and machine intelligence 2022 (ISCMI 2022), 26-27 November 2022, Toronto, Candada. Piscataway: IEEE [online], pages 129-134. Available from: https://doi.org/10.1109/iscmi56532.2022.10068436

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely p... Read More about Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation..

Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. (2022)
Journal Article
RAJI, I.D., BELLO-SALAU, H., UMOH, I.J., ONUMANYI, A.J., ADEGBOYE, M.A. and SALAWUDEEN, A.T. 2022. Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. Applied sciences [online], 12(3), article 1186. Available from: https://doi.org/10.3390/app12031186

Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance... Read More about Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models..