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Optimization of C5.0 Classifier With Bayesian Theory for Food Traceability Management Using Internet of Things

Optimization of C5.0 Classifier With Bayesian Theory for Food Traceability Management Using Internet of Things

Balamurugan Souprayen, Ayyasamy Ayyanar, Suresh Joseph K
Copyright: © 2020 |Volume: 1 |Issue: 1 |Pages: 21
ISSN: 2644-1845|EISSN: 2644-1853|EISBN13: 9781799804048|DOI: 10.4018/IJSSTA.2020010101
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MLA

Souprayen, Balamurugan, et al. "Optimization of C5.0 Classifier With Bayesian Theory for Food Traceability Management Using Internet of Things." IJSSTA vol.1, no.1 2020: pp.1-21. http://doi.org/10.4018/IJSSTA.2020010101

APA

Souprayen, B., Ayyanar, A., & Suresh Joseph K. (2020). Optimization of C5.0 Classifier With Bayesian Theory for Food Traceability Management Using Internet of Things. International Journal of Smart Sensor Technologies and Applications (IJSSTA), 1(1), 1-21. http://doi.org/10.4018/IJSSTA.2020010101

Chicago

Souprayen, Balamurugan, Ayyasamy Ayyanar, and Suresh Joseph K. "Optimization of C5.0 Classifier With Bayesian Theory for Food Traceability Management Using Internet of Things," International Journal of Smart Sensor Technologies and Applications (IJSSTA) 1, no.1: 1-21. http://doi.org/10.4018/IJSSTA.2020010101

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Abstract

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction. The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss, and reduce system complexity. The primary issue is to tackle current limitations to prevent food defects from exceeding hazardous levels and to inform the safety measures to the customers. The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

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