A System for Fuel Distribution in Nigeria Based on Statistical Computer Machine Intelligence Learning Algorithm

I. C. Emeto, Galadima A.a, E.N. Osegi, Ajayi C.o, S Ogbonna, Gidado S.m and D.C. Elenwo
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I. C. Emeto: Department of Cybersecurity, Federal University of Technology Owerri, Nigeria.
Galadima A.a: Department of Cybersecurity, Federal University of Technology Owerri, Nigeria.
E.N. Osegi: Department of Information Technology, National Open University of Nigeria, Lagos State, Nigeria
Ajayi C.o: Department of Computer Science, University of Kashere, Gombe, Nigeria.
S Ogbonna: Department of Computer Science, College of Fisheries and Marine Technology, Lagos, Nigeria.
Gidado S.m: Department of Computer Science, Federal polytechnic kaltungo, Gombe State Nigeria.
D.C. Elenwo: Department of Computer Science, College of Fisheries and Marine Technology, Lagos, Nigeria.

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 10, 265-270

Abstract: The current problem of fuel scarcity in Nigeria and the drawbacks associated with it bearing in mind the undesirable effects it has on the economy, transport sector and the small and medium scale enterprises cannot be overemphasized. In this paper, we present the situation of PMS distribution in the Nigerian state using a monitoring tool based on machine intelligence and human-like statistical learning system, the numerical deviant learning algorithm (n-DLA). Specifically, this algorithm is a variant of a cortical-like algorithm based on artificial (machine) intelligence technique. Experiments with this algorithm showed that price hike cannot be avoided in the months that follow due to abnormal distribution of product unless a drastic action is taken by the operators to avert the situation. This approach can be a useful tool in predicting in advance the month that may have a high likelihood of a hike in the pump price of PMS in addition to its distribution.

Date: 2024
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