An Intelligent Neural Network Algorithm for Uncertainty Handling in Sensor Failure Scenario of Food Quality Assurance Model

S.N. Deepa; N. Yogambal Jayalakshmi

doi:10.24423/cames.409

S.N. Deepa National Institute of Technology Arunachal Pradesh
N. Yogambal Jayalakshmi Dr. Mahalingam College of Engineering and Technology

DOI: http://dx.doi.org/10.24423/cames.409

Abstract

The quality of food is usually tested by sensing the product odor using e-nose technique. However, in a real-time testing environment, some of the employed sensors may fail to operate, which imposes great uncertainty on the food quality assurance model. To handle the uncertainty, a support vector machine (SVM) classifier algorithm is developed to deal with the failure sensor effect using a data imputation strategy. The proposed model is evaluated experimentally by means of benchmark datasets, and validated in a realtime environment by programming an Arduino-UNO controller in the internet of things (IoT) environment.

Keywords

e-nose, data imputation, quality assurance, multiclass SVM, k-nearest neighbor, IoT, Arduino UNO,

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