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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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Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

2024Journal of Computing Theories and ApplicationsJournalOpen access

10.62411/jcta.10323 Full text42 citations

Authors

8 authors across 7 institutions in 2 countries.

Fidelis Obukohwo AghwareFirst author
Arnold Adimabua Ojugo
- Federal University of Petroleum Resource Effurun (NG)
Wilfred Adigwe
- Delta State Polytechnic Ogwashi-Uku (NG)
- Delta State University (NG)
Christopher Chukwufumaya Odiakaose
- Western Delta University (NG)
- Dennis Osadebay University (NG)
Emma Obiajulu Ojei
Nwanze Chukwudi Ashioba
- Dennis Osadebay University (NG)
Margareth Dumebi Okpor
Victor Ochuko GetelomaSenior author
- University of Delta Agbor (NG)

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Imbalanced Data Classification Techniqueslevel 3 · 1.00
Vehicle License Plate Recognitionlevel 3 · 0.97
Financial Distress and Bankruptcy Predictionlevel 3 · 0.97

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2024 · Journal of Computing Theories and Applications · 42 citations
https://openalex.org/W4393196039

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection · Sankore