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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Andrew Okonji Eboka
Federal College of Education Technical
Computer Science & IT

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Improved distribution and food safety for beef processing and management using a blockchain-tracer support framework Arnold Adimabua Ojugo; Patrick Ogholuwarami Ejeh; Odiakaose Chukwufunaya Christopher; Andrew Okonji Eboka; Frances Uchechukwu Emordi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v12i3.pp205-213

Abstract

Agriculture has since become a major source of livelihood for Nigerians. It also accounts for over 85% of the total food consumed within her borders. The sector has maintained improved productivity and profitability via a concerted effort to address critical issues such as an unorganized regulatory system, lack of food safety data, no standards in agricultural produce, non-adaptation to precision farming, and non-harmony via inventory trace supports. This study proposes blockchain-based trace-support in a continued effort to ensure food quality, consumer safety, and trading of food assets. It uses the radio frequency identification (RFID) sensor to register and track livestocks, farms/farmers, and abattoir processes as well as provisions a databank to trace livestock data. Results show the model adequately perform about 1,101 transactions per seconds with a response time of 0.21 s for queries and 0.28 s for https pages respectively for 2,500 users. Also, it yields a slightly longer time of 0.32 s for queries and 0.38 s for https pages respectively with an increased 5,000 users via the world-state as stored in the blockchain’s hyper-fabric ledger. Overall, the framework can directly query and retrieve data without it traversing the whole ledger. This, in turn, improves the efficiency and effectiveness of the traceability system.
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble Arnold Adimabua Ojugo; Patrick Ogholuwarami Ejeh; Christopher Chukwufunaya Odiakaose; Andrew Okonji Eboka; Frances Uchechukwu Emordi
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.
Co-Authors Arnold Adimabua Ojugo Arnold Adimabua Ojugo Christopher Chukwufunaya Odiakaose Frances Uchechukwu Emordi Odiakaose Chukwufunaya Christopher Patrick Ogholuwarami Ejeh