All Outputs (10)

COIL Match Maker: a new software application to facilitate COIL collaboration. (2024)
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..

Assessing the research scene of green AI via bibliometric analysis. (2024)
Presentation / Conference Contribution
ABDULMALIK, M.R., IBEKE, E., EZENKWU, C.P. and IWENDI, C. [2024]. Assessing the research scene of green AI via bibliometric analysis. To be published in the Proceedings of the 2024 International conference on advances in communication technology and computer engineering (ICACTCE'24), 29-30 November 2024, Marrakech, Morocco. Lecture notes in networks and systems (LNNS). Cham: Springer [online], (accepted). To be made available from: https://www.springer.com/series/15179

The environmental impact of artificial intelligence (AI) continues to rise as more people embrace the technology. The optimization of AI models to be more efficient, use less energy, and emit low carbon is essential. This bibliometric study presents... Read More about Assessing the research scene of green AI via bibliometric analysis..

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 [online], article 10739320. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739320

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 [online], article 10739123. Available from: https://doi.org/10.1109/ICEECT61758.2024.10739123

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..

Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario. (2024)
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..

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..

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..

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..