All Outputs (5)

A green AI model selection strategy for computer-aided mpox detection. (2023)
Presentation / Conference Contribution
EZENKWU, C.P., STEPHEN, B.U.-A., AFFIAH, I. and DANIEL, B. 2023. A green AI model selection strategy for computer-aided mpox detection. In Proceedings of the 16th IEEE Africon conference (IEEE AFRICON 2023): advancing technology in Africa towards presence on the global stage, 20-22 September 2023, Nairobi, Kenya. Piscataway: IEEE [online], document number 10293707. Available from: https://doi.org/10.1109/AFRICON55910.2023.10293707

With the recent global surge in mpox (formerly monkeypox) cases, researchers have proposed deep learning technologies for early detection of the disease from skin lesion images. However, many of these researchers follow the current Red AI trend of se... Read More about A green AI model selection strategy for computer-aided mpox detection..

Towards expert systems for improved customer services using ChatGPT as an inference engine. (2023)
Presentation / Conference Contribution
EZENKWU, C.P. 2023. Towards expert systems for improved customer services using ChatGPT as an inference engine. In Proceedings of the 2023 IEEE (Institute of electrical and Electronics Engineers) International conference on digital applications, transformation and economy (ICDATE 2023), 14-16 July 2023, Miri, Malaysia, article 10248647. Available from: https://doi.org/10.1109/ICDATE58146.2023.10248647

By harnessing both implicit and explicit customer data, companies can develop a more comprehensive understanding of their consumers, leading to better customer engagement and experience, and improved loyalty. As a result, businesses have embraced man... Read More about Towards expert systems for improved customer services using ChatGPT as an inference engine..

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..

Towards autonomous developmental artificial intelligence: case study for explainable AI. (2023)
Presentation / Conference Contribution
STARKEY, A. and EZENKWU, C.P. 2023. Towards autonomous developmental artificial intelligence: case study for explainable AI. In Maglogiannis, I., Iliadis, L., MacIntyre, J. and Dominguez, M. (eds.) Artificial intelligence applications and innovations: proceedings of the 19th IFIP (International Federation for Information Processing) WG 12.5 Artificial intelligence applications and innovations international conference (AIAI 2023), 14-17 June 2023, León, Spain. IFIP advances in information and communication technology, 676. Cham: Springer [online], pages 94-105. Available from: https://doi.org/10.1007/978-3-031-34107-6_8

State-of-the-art autonomous AI algorithms such as reinforcement learning and deep learning techniques suffer from high computational complexity, poor explainability ability, and a limited capacity for incremental adaptive learning. In response to the... Read More about Towards autonomous developmental artificial intelligence: case study for explainable AI..

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..