All Outputs (4)

Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. (2024)
Journal Article
YOUCEFI, M.R., BOUKREDERA, F.S., GHALEM, K., HADJADJ, A. and EZENKWU, C.P. [2024]. Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement. Applied intelligence [online], Latest Articles. Available from: https://doi.org/10.1007/s10489-024-05560-5

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the sc... Read More about Development of an expert-informed rig state classifier using naive Bayes algorithm for invisible loss time measurement..

Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario. (2024)
Presentation / Conference
EZENKWU, C.P., IBEKE, E. and IWENDI, C. 2024. Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario. To be presented at the 3rd International conference on advanced communication and intelligent systems (ICACIS 2024), 16-17 May 2024, New Delhi, India.

This study addresses the issue of recognising customer intent when only limited training data is available. The performance of ChatGPT was evaluated in this scenario, and it was found to be better than traditional machine learning algorithms and the... Read More about Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario..

Advancing AI with green practices and adaptable solutions for the future. [Article summary] (2024)
Digital Artefact
STARKEY, A. and EZENKWU, C.P. 2024. Advancing AI with green practices and adaptable solutions for the future. [Article summary]. Posted on The Academic [online], 28 March 2024. Available from: https://theacademic.com/ai-green-practices-adaptable-solutions/

Despite AI's achievements, how can its limitations be addressed to reduce computational costs, enhance transparency and pioneer eco-friendly practices?

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..