All Outputs (6)

Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction (2022)
Journal Article
Oyedele, A. A., Ajayi, A., Oyedele, A., Bello, S., & Oyedele, L. (2023). Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction. Expert Systems with Applications, 213(Part C), Article 119233. https://doi.org/10.1016/j.eswa.2022.119233

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial het... Read More about Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction.

Robotics in construction: A critical review of the reinforcement learning and imitation learning paradigms (2022)
Journal Article
Davila Delgado, M., & Oyedele, L. (2022). Robotics in construction: A critical review of the reinforcement learning and imitation learning paradigms. Advanced Engineering Informatics, 54, Article 101787. https://doi.org/10.1016/j.aei.2022.101787

The reinforcement and imitation learning paradigms have the potential to revolutionise robotics. Many successful developments have been reported in literature; however, these approaches have not been explored widely in robotics for construction. The... Read More about Robotics in construction: A critical review of the reinforcement learning and imitation learning paradigms.

A deep learning approach to concrete water-cement ratio prediction (2022)
Journal Article
Oyedele, L., Bello, S., Olaitan, O. K., Olonade, K. A., Olajumoke, A. M., Ajayi, A., …Bello, A. L. (2022). A deep learning approach to concrete water-cement ratio prediction. Results in Materials, 15(September 2022), Article 100300. https://doi.org/10.1016/j.rinma.2022.100300

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which... Read More about A deep learning approach to concrete water-cement ratio prediction.

A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways (2022)
Journal Article
Akinosho, T. D., Oyedele, L. O., Bilal, M., Barrera-Animas, A. Y., Gbadamosi, A. Q., & Olawale, O. A. (2022). A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways. Ecological Informatics, 69, Article 101609. https://doi.org/10.1016/j.ecoinf.2022.101609

The construction of intercity highways by the government has resulted in a progressive increase in vehicle emissions and pollution from noise, dust, and vibrations despite its recognition of the air pollution menace. Efforts that have targeted roadsi... Read More about A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways.

Life cycle optimisation of building retrofitting considering climate change effects (2022)
Journal Article
Luo, X. J., & Oyedele, L. O. (2022). Life cycle optimisation of building retrofitting considering climate change effects. Energy and Buildings, 258, 111830. https://doi.org/10.1016/j.enbuild.2022.111830

Novelty: Climate change has significant impacts on building energy performance. A novel life cycle optimisation strategy is developed for determining optimal retrofitting solutions for office buildings with climate change effects taken into considera... Read More about Life cycle optimisation of building retrofitting considering climate change effects.

A self-adaptive deep learning model for building electricity load prediction with moving horizon (2022)
Journal Article
Luo, X., & Oyedele, L. (2022). A self-adaptive deep learning model for building electricity load prediction with moving horizon. Machine Learning with Applications, 7, Article 100257. https://doi.org/10.1016/j.mlwa.2022.100257

A self-adaptive deep learning model powered by ranking selection-based particle swarm optimisation (RSPSO) is developed to predict electricity load in buildings with moving horizons. The main features of the load prediction model include its self-ada... Read More about A self-adaptive deep learning model for building electricity load prediction with moving horizon.