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 Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Jote
N. Yogambal Jayalakshmi Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Coimbatore

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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