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All Outputs (4)

Analysing the predictivity of features to characterise the search space (2022)
Conference Proceeding
Durgut, R., Aydin, M. E., Ihshaish, H., & Rakib, A. (2022). Analysing the predictivity of features to characterise the search space. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (1-13). https://doi.org/10.1007/978-3-031-15937-4_1

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised... Read More about Analysing the predictivity of features to characterise the search space.

Experimental and numerical study of hybrid (CFRP-GFRP) composite laminates containing circular cut-outs under shear loading (2022)
Journal Article
Damghani, M., Ali, R., Murphy, A., & Fotouhi, M. (2022). Experimental and numerical study of hybrid (CFRP-GFRP) composite laminates containing circular cut-outs under shear loading. Thin-Walled Structures, 179(October 2022), 109752. https://doi.org/10.1016/j.tws.2022.109752

Previous works have established the response and failure behaviour of hybrid (CFRP-GFRP) laminates when subjected to a wide range of destabilising loads. However, to date no works have focused on plates with cut-outs under shear loading and quantifie... Read More about Experimental and numerical study of hybrid (CFRP-GFRP) composite laminates containing circular cut-outs under shear loading.

Transfer learning for operator selection: A reinforcement learning approach (2022)
Journal Article
Durgut, R., Aydin, M. E., & Rakib, A. (2022). Transfer learning for operator selection: A reinforcement learning approach. Algorithms, 15(1), Article 24. https://doi.org/10.3390/a15010024

In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a par... Read More about Transfer learning for operator selection: A reinforcement learning approach.

Memory-constrained context-aware reasoning (2022)
Conference Proceeding
Uddin, I., Rakib, A., Ali, M., & Vinh, P. C. (2022). Memory-constrained context-aware reasoning. In P. Cong Vinh, & A. Rakib (Eds.), Context-Aware Systems and Applications (133-146). https://doi.org/10.1007/978-3-030-93179-7_11

The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, includ... Read More about Memory-constrained context-aware reasoning.