All Outputs (18)

Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). (2024)
Presentation / Conference Contribution
UGBOMEH, O., YIYE, V., IBEKE, E., EZENKWU, C.P., SHARMA, V. and ALKHAYYAT, A. [2024]. Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE. (Forthcoming)

Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This resea... Read More about Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI)..

Investigating key contributors to hospital appointment no-shows using explainable AI. (2024)
Presentation / Conference Contribution
YIYE, V., UGBOMEH, O., EZENKWU, C.P., IBEKE, E., SHARMA, V. and ALKHAYYAT, A. [2024]. Investigating key contributors to hospital appointment no-shows using explainable AI. In Proceedings of the 2024 International conference on electrical, electronics and computing technologies (ICEECT 2024), 29-31 August 2024, Greater Noida, India. Piscataway: IEEE. (Forthcoming)

The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictor... Read More about Investigating key contributors to hospital appointment no-shows using explainable AI..

Cost optimisation in offshore wind through procurement data analytics. (2024)
Presentation / Conference Contribution
SHITTU, Q. and EZENKWU, C.P. 2024. Cost optimisation in offshore wind through procurement data analytics. In Arai, K. (eds.) Intelligent computing: proceedings of the 12th Computing conference 2024 (Computing 2024), 11-12 July 2024, London, UK. Lecture notes in networks and systems, 1019. Cham: Springer [online], volume 4, pages 80-98. Available from: https://doi.org/10.1007/978-3-031-62273-1_6

Governments have implemented a variety of national and international efforts to reduce carbon emissions (so as to prevent the damaging effects of climate change on the environment and the global economy) through the execution of several policies, inc... Read More about Cost optimisation in offshore wind through procurement data analytics..

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], 54(17-18), pages 7659-7673. 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..

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..

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..

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..

Machine autonomy: definition, approaches, challenges and research gaps. (2019)
Presentation / Conference Contribution
EZENKWU, C.P. and STARKEY, A. 2019. Machine autonomy: definition, approaches, challenges and research gaps. In Arai, K., Bhatia, R. and Kapoor, S. (eds.) Intelligent computing: proceedings of the 2019 Computing conference, 16-17 July 2019, London, UK. Advances in intelligent systems and computing, 997. Cham: Springer [online], volume 1, pages 335-358. Available from: https://doi.org/10.1007/978-3-030-22871-2

The processes that constitute the designs and implementations of AI systems such as self-driving cars, factory robots and so on have been mostly hand-engineered in the sense that the designers aim at giving the robots adequate knowledge of its world.... Read More about Machine autonomy: definition, approaches, challenges and research gaps..

Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario.
Presentation / Conference Contribution
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..

COIL Match Maker: a new software application to facilitate COIL collaboration.
Presentation / Conference Contribution
CRAWFORD, I. and EZENKWU, P. [2024]. COIL Match Maker: a new software application to facilitate COIL collaboration. To be presented at the 6th International virtual exchange conference (IVEC 2024), 21-24 October 2024, [virtual event].

COIL Match Maker is proposed as a new AI-powered software application that is designed to make the process of finding a COIL partner and creating a COIL project faster, simpler and more accessible - regardless of location, prior experience, or availa... Read More about COIL Match Maker: a new software application to facilitate COIL collaboration..