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Journal : International Journal of Informatics and Communication Technology (IJ-ICT)

BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria Aghware, Fidelis Obukohwo; Adigwe, Wilfred; Okpor, Margareth Dumebi; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Eboka, Andrew Okonji; Ejeh, Patrick Ogholorunwalomi; Taylor, Onate Egerton; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp178-187

Abstract

With population rising to approximately 200 million Nigerians – fast-paced, urbanization has continued to advent food insecurity with maladministration, corruption, internal rife, and starvation. These, threatened the nation's unity with the lockdown of 2020; and consequently, have now become the trend. Nigeria must as a nation, re-examine her methods in the administration of palliatives (in lieu of food and relief) distribution – as the above-listed issues have become of critical need in the equitable distribution of reliefs, both from the humanitarian agency view, and the Government (State and Federal). They have noticed non-transparency, corruption, and data inadequacies, as major drawbacks in its management. Our study presents a blockchain ensemble for the administration of food palliatives distribution in Nigeria that first ensures, that all beneficiaries be registered, and the food palliatives are sensor-tagged and recorded on the blockchain. Results show the number of transactions per second and page retrieval abilities for the proposed chain were quite low with 30-TPS and 0.38seconds respectively – as compared to public blockchain. Proposed ensemble eliminates fraud that is herein rippled across the existing system, minimizes corrupt practices via sensor-based model, provides insight for stakeholders, and minimize the error in reported data on the supply chain.
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble Ojugo, Arnold Adimabua; Ejeh, Patrick Ogholuwarami; Odiakaose, Christopher Chukwufunaya; Eboka, Andrew Okonji; Emordi, Frances Uchechukwu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp108-115

Abstract

Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management.