Outputs (7)

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. (2024)
Journal Article
EZENKWU, C.P., CANNON, S. and IBEKE, E. 2024. Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. Environmental monitoring and assessment [online], 196(3), article number 231. Available from: https://doi.org/10.1007/s10661-024-12388-6

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change... Read More about Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies..

Enhancing the drilling efficiency through the application of machine learning and optimization algorithm. (2023)
Journal Article
BOUKREDERA, F.S., YOUCEFI, M.R., HADJADJ, A., EZENKWU, C.P., VAZIRI, V. and APHALE, S.S. 2023. Enhancing the drilling efficiency through the application of machine learning and optimization algorithm. Engineering applications of artificial intelligence [online], 126(part C), article 107035. Available from: https://doi.org/10.1016/j.engappai.2023.107035

This article presents a novel Artificial Intelligence (AI) workflow to enhance drilling performance by mitigating the adverse impact of drill-string vibrations on drilling efficiency. The study employs three supervised machine learning (ML) algorithm... Read More about Enhancing the drilling efficiency through the application of machine learning and optimization algorithm..

Automated well-log pattern alignment and depth-matching techniques: an empirical review and recommendations. (2023)
Journal Article
EZENKWU, C.P., GUNTORO, J., STARKEY, A., VAZIRI, V. and ADDARIO, M. 2023. Automated well-log pattern alignment and depth-matching techniques: an empirical review and recommendations. Petrophysics [online], 64(1), pages 115-129. Available from: https://doi.org/10.30632/PJV64N1-2023a9

Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well's history. However, the traditional human-reliant approach to well-log interpretation, which has been the most com... Read More about Automated well-log pattern alignment and depth-matching techniques: an empirical review and recommendations..

An unsupervised autonomous learning framework for goal-directed behaviours in dynamic contexts. (2022)
Journal Article
EZENKWU, C.P. and STARKEY, A. 2022. An unsupervised autonomous learning framework for goal-directed behaviours in dynamic contexts. Advances in computational intelligence [online], 2(3), article number 26. Available from: https://doi.org/10.1007/s43674-022-00037-9

Due to their dependence on a task-specific reward function, reinforcement learning agents are ineffective at responding to a dynamic goal or environment. This paper seeks to overcome this limitation of traditional reinforcement learning through a tas... Read More about An unsupervised autonomous learning framework for goal-directed behaviours in dynamic contexts..

A class-specific metaheuristic technique for explainable relevant feature selection. (2021)
Journal Article
EZENKWU, C.P., AKPAN, U.I. and STEPHEN, B.U.-A. 2021. A class-specific metaheuristic technique for explainable relevant feature selection. Machine learning with applications [online], 6, article number 100142. Available from: https://doi.org/10.1016/j.mlwa.2021.100142

A significant amount of previous research into feature selection has been aimed at developing methods that can derive variables that are relevant to an entire dataset. Although these approaches have revealed substantial improvements in classification... Read More about A class-specific metaheuristic technique for explainable relevant feature selection..

Community informatics for sustainable management of pandemics in developing countries: a case study of COVID-19 in Nigeria. (2021)
Journal Article
EZE, P.U., EZENKWU, C.P. and ETTEH, C.C. 2021. Community informatics for sustainable management of pandemics in developing countries: a case study of COVID-19 in Nigeria. Ethics, medicine and public health [online], 16, article number 100632. Available from: https://doi.org/10.1016/j.jemep.2021.100632

Although a significant number of the human population in developing countries live in urban communities, majority of the population lives in rural areas. Developing countries, especially in their rural areas, suffer from a lack of healthcare faciliti... Read More about Community informatics for sustainable management of pandemics in developing countries: a case study of COVID-19 in Nigeria..

Unsupervised temporospatial neural architecture for sensorimotor map learning. (2019)
Journal Article
EZENKWU, C.P. and STARKEY, A. 2021. Unsupervised temporospatial neural architecture for sensorimotor map learning. IEEE transactions on cognitive and developmental systems [online], 13(1), pages 223-230. Available from: https://doi.org/10.1109/TCDS.2019.2934643

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agent's world and equip it... Read More about Unsupervised temporospatial neural architecture for sensorimotor map learning..