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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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ORCID — researcher identity
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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Enoima Essien Umoh · Sankore

University of Cross River State

Enoima Essien Umoh

Name matching only

This profile was assembled from author names on publications. Open indexes routinely split one researcher across several such profiles, so these totals may be incomplete. 2 other profiles were folded into this one.

Publications

5

Citations

0

h-index

0

Computed over the works the graph holds, so it is a floor rather than a career figure.

i10-index

0

Works published 2022 onward have been swept for this country. Figures are complete within that window, to the extent the open indexes are.

Publications

5 works in the graph, newest first.

Adapting Foundational ASR Models to Efik: An Empirical Study of an Extremely Low-Resource Tonal Language
2026PublicationOpen access0 citations10.63317/3wmtoxexdady
Forecast analysis of call setup success rate in mobile networks: A hybrid approach
2025International Journal of Science and Research ArchiveJournalOpen accessFirst authorCorresponding0 citations10.30574/ijsra.2025.16.2.2373
Machine learning-based forecast modelling of dropped call rate in mobile networks: A random forest and support vector regression approach
2025Open Access Research Journal of Science and TechnologyJournalOpen accessFirst authorCorresponding0 citations10.53022/oarjst.2025.14.2.0107
Prediction Modelling for Forecast of Standalone Dedicated Control Channel Congestion using Machine Learning
2025GSC Advanced Research and ReviewsJournalOpen accessFirst authorCorresponding0 citations10.30574/gscarr.2025.24.3.0264
A Machine Learning Approach to Forecasting TCCH CongestiA Machine Learning Approach to Forecasting TCCH Congestion in Nigerian GSM Networks: Comparing Supporton in Nigerian GSM Networks: Comparing Support Vector Regression and Gradient Boosting Regression
2025International Journal of Science and Research ArchiveJournalOpen accessSenior author0 citations10.30574/ijsra.2025.16.3.2635