Hexokinase-I anchors to voltage-dependent anion channel 1 (VDAC1) at a membrane-buried glutamate, E73, and disrupting this contact is an established anticancer objective. The site-directed docking of natural product libraries into such hotspots routinely identifies disruptors, yet these candidates are rarely tested for...
R. T. Aruleba, Ebenezer Esenogho· Biophysica· 0 citations
Alzheimer’s disease and dementia are leading causes of cognitive decline among older adults, creating a need for accurate, transparent, and clinically interpretable decision-support tools for routine screening. This study proposes a leakage-aware and explainable machine learning framework for dementia classification us...
G. Obaido, Ebenezer Esenogho· Mathematics· 0 citations
Paratuberculosis is a chronic infectious disease associated with substantial economic losses in livestock production. Histopathological examination remains an important diagnostic approach but can be time-consuming and dependent on specialist expertise, motivating the development of automated and interpretable image-cl...
Nokulunga Nhlapho, G. Obaido, Ebenezer Esenogho· Bioengineering· 0 citations
Reliable estimation of radio wave propagation across irregular terrain is essential for the effective planning and optimization of contemporary wireless communication systems, such as cellular networks, broadcasting infrastructure, and radar systems. The intricate interaction between electromagnetic waves and environme...
O. J. Famoriji, Michael O. Omojoyegbe, Ebenezer Esenogho· Telecom· 0 citations
The incorporation of Distributed Energy Resources (DER), especially PV and BESS, is essential in making radial power systems stable. Unfortunately, most recent optimization algorithms make an assumption of an unconstrained utility supply, targeting minimization of losses during daylight or only profit-making energy tra...
T. Somefun, S. A. Aransiola, Tshepo Cameron Modisane et al.· Journal of Electrical System...· 0 citations
A unique approach to Explainable Artificial Intelligence (XAI) for 5G networks using Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME), not only to discover anomalies but also to explain them coherently while providing intuitive interpretations.
G. A. Ajibade, Ebenezer Esenogho, Cameron Modisane· IEEE Access· 0 citations
This framework illustrates, without any claim of clinical validity, how a leakage-safe ML pipeline and SHAP interpretability can be combined and rigorously self-audited; real patient-level data and external validation are required before any clinical inference is drawn.
David Chepkonga, A. Langat, Ebenezer Esenogho et al.· Asian Journal of Research in...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
