All Outputs (4)

ClusterSwarm: cluster-specific feature selection using binary particle swarm optimisation. (2025)
Journal Article
EZENKWU, C.P., STARKEY, A. and AZIZ, A.A. [2025]. ClusterSwarm: cluster-specific feature selection using binary particle swarm optimisation. Computing [online], (accepted).

Feature selection has become an important step in machine learning pipelines, contributing to model interpretability and accuracy. While the emphasis has been hugely on global feature selection techniques, these methods do not support feature attribu... Read More about ClusterSwarm: cluster-specific feature selection using binary particle swarm optimisation..

Assessing the research scene of green AI via bibliometric analysis. (2025)
Presentation / Conference Contribution
ABDULMALIK, M.R., IBEKE, E., EZENKWU, C.P. and IWENDI, C. [2025]. Assessing the research scene of green AI via bibliometric analysis. In Iwendi, C., Boulouard, Z. and Kryvinska, N. (eds.) Proceedings of the 4th International conference on advances in communication technology and computer engineering (ICACTCE'24): transforming industries: harnessing the power of artificial intelligence and the internet of things, 29-30 November 2024, Marrakech, Morocco. Lecture notes in networks and systems (LNNS), 1313. Cham: Springer [online], volume 2, (forthcoming). To be made available from: https://link.springer.com/book/9783031946226

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..

Evaluating cross-domain sentiment analysis using convolutional neural network for Amazon dataset. (2025)
Journal Article
AZIZ, A.A., OTHMAN, A.N., EZENKWU, P. and MADI, E.N. 2025. Evaluating cross-domain sentiment analysis using convolutional neural network for Amazon dataset. Journal of advanced research in applied sciences and engineering technology [online], 63(2), pages 207-214. Available from: https://doi.org/10.37934/araset.63.2.207214

Sentiment Analysis (SA) has garnered extensive research attention over the past decades as a means to comprehend users' attitudes and opinions in various domains. With the proliferation of online communities and the rapid generation of social media c... Read More about Evaluating cross-domain sentiment analysis using convolutional neural network for Amazon dataset..

Exploring engineering students' perceptions of AI in education with the technology acceptance model (TAM). [Dataset] (2025)
Data
ABOLLE-OKOYEAGU, J., IBEKE, E., ONOJA, T., EZENKWU, P.C. and EZEONWUMELU, V. 2025. Exploring engineering students' perceptions of AI in education with the Technology Acceptance Model (TAM). [Dataset]. Hosted on Mendeley Data [online], version 1. Available from: https://doi.org/10.17632/b3jbzgnrwv.1

This research investigates perceptions of Artificial Intelligence (AI) in education through the framework of the Technology Acceptance Model (TAM).