Dr Ebuka Ibeke's Outputs (6)

A systematic review on blockchain-based access control systems in cloud environment. (2024)
Journal Article
PUNIA, A., GULIA, P., GILL, N.S., IBEKE, E., IWENDI, C. and SHUKLA, P.K. 2024. A systematic review on blockchain-based access control systems in cloud environment. Journal of cloud computing [online], (accepted).

The widespread adoption of cloud computing has dramatically altered how data is stored, processed, and accessed in an era. The rapid development of digital technologies characterizes all this. The widespread adoption of cloud services has introduced... Read More about A systematic review on blockchain-based access control systems in cloud environment..

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..

CIA security for internet of vehicles and blockchain-AI integration. (2024)
Journal Article
HAI, T., AKSOY, M., IWENDI, C., IBEKE, E. and MOHAN, S. 2024. CIA security for internet of vehicles and blockchain-AI integration. Journal of grid computing [online], 22(2), article number 43. Available from: https://doi.org/10.1007/s10723-024-09757-3

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of... Read More about CIA security for internet of vehicles and blockchain-AI integration..

Using entropy to measure text readability in Bahasa Malaysia for year one students. (2024)
Journal Article
BARAWI, M.H., OSMAN, S.N.M., ABD YUSOF, N.F., IBEKE, E. and FADHLI, M. 2024. Using entropy to measure text readability in Bahasa Malaysia for year one students. Journal of cognitive sciences and human development [online], 10(1), pages 103-123. Available from: https://doi.org/10.33736/jcshd.6817.2024

Text readability is essential for effective learning and communication, especially for beginner readers. However, there are no known measures to calculate the readability of Bahasa Malaysia, the national language of Malaysia. This research proposes a... Read More about Using entropy to measure text readability in Bahasa Malaysia for year one students..

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..