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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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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.

Fault prediction model on electrical power network using artificial neural network-based time series: A case study of Ayede-Eruwa/ Lanlate Feeder

2022International Journal of Frontline Research in Engineering and TechnologyJournalOpen access

10.56355/ijfret.2022.1.1.0004 Full text0 citations

Authors

4 authors across 2 institutions in 1 country.

Bolarinwa Samson AdelekeFirst authorCorresponding
- The Federal Polytechnic, Ado-Ekiti (NG)
Adewale Abayomi Alabi
- The Federal Polytechnic, Ado-Ekiti (NG)
Sunday Oyinlola Ogundoyin
- University of Ibadan (NG)
Ademola Adekunle AraromiSenior author
- The Federal Polytechnic, Ado-Ekiti (NG)

Fields

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

Energy Load and Power Forecastinglevel 3 · 1.00
Electricity Theft Detection Techniqueslevel 3 · 0.98
Power System Reliability and Maintenancelevel 3 · 0.98

Sources

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

openalexseen September 9, 2026
2022 · International Journal of Frontline Research in Engineering and Technology · 0 citations
https://openalex.org/W4289529520

Fault prediction model on electrical power network using artificial neural network-based time series: A case study of Ayede-Eruwa/ Lanlate Feeder · Sankore