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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics
Adimabua Ojugo, Arnold
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Computer Science & IT Electrical & Electronics Engineering

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Improved distribution and food safety for beef processing and management using a blockchain-tracer support framework Adimabua Ojugo, Arnold; Ogholuwarami Ejeh, Patrick; Chukwufunaya Christopher, Odiakaose; Okonji Eboka, Andrew; Uchechukwu Emordi, Frances
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.
Unmasking effects of feature selection and SMOTE-Tomek in tree-based random forest for scorch occurrence detection Dumebi Okpor, Margaret; Eluemnor Anazia, Kizito; Adigwe, Wilfred; Abugor Okpako, Ejaita; Moses Setiadi, De Rosal Ignatius; Adimabua Ojugo, Arnold; Omoruwou, Felix; Erhovwo Ako, Rita; Ochuko Geteloma, Victor; Valentine Ugbotu, Eferhire; Chukwudi Aghaunor, Tabitha; Enadona Oweimeito, Amanda
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8901

Abstract

Scorch occurrence during the production of flexible polyurethane foam has been a menace that consistently, jeopardize a foam’s integrity and resilience. It leads to foam suppression and compactness integrity failure due to scorch. There is always the increased likelihood of scorching, and makes crucial the utilization of methods that seek to avert it. Studies predict that the formation of foam constituent processes via optimization using machine learning have adequately trained models to effectively identify scorch occurrence during the profiling in the polyurethane foam production. Our study utilizes the random forest (RF) ensemble with feature selection (FS) and data balancing technique to identify production predictors. Study yields accuracy of 0.9998 with F1-score of 0.9819. Model yields 2-distinct cases for (non)-occurrence of scorch respectively, and the ensemble demonstrates that it can effectively and efficiently predict the occurrence of scorch in the production of flexible polyurethane foam manufacturing process.
Co-Authors Abugor Okpako, Ejaita Adigwe, Wilfred Chukwudi Aghaunor, Tabitha Chukwufunaya Christopher, Odiakaose De Rosal Ignatius Moses Setiadi Dumebi Okpor, Margaret Eluemnor Anazia, Kizito Enadona Oweimeito, Amanda Erhovwo Ako, Rita Ochuko Geteloma, Victor Ogholuwarami Ejeh, Patrick Okonji Eboka, Andrew Omoruwou, Felix Uchechukwu Emordi, Frances Valentine Ugbotu, Eferhire