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

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

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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Published
Mar 21, 2022
How to Cite
DEEPA, S.N.; JAYALAKSHMI, N. Yogambal. An Intelligent Neural Network Algorithm for Uncertainty Handling in Sensor Failure Scenario of Food Quality Assurance Model. Computer Assisted Methods in Engineering and Science, [S.l.], v. 29, n. 1–2, p. 105–123, mar. 2022. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/409>. Date accessed: 28 may 2022. doi: http://dx.doi.org/10.24423/cames.409.