Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Ezenkwu, Chinedu Pascal; Cannon, San; Ibeke, Ebuka

doi:10.1007/s10661-024-12388-6

All Outputs (5)

Machine learning algorithms for stroke risk prediction leveraging on explainable artificial intelligence techniques (XAI). (2024)
Conference Proceeding
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)
Conference Proceeding
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..

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..

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..