An intelligent neural network algorithm for uncertainty handling in sensor failure scenario of food quality assurance model

• Autorzy: Deepa S. N. , Jayalakshmi N. Yogamba

Identyfikatory

DOI

10.24423/cames.409

• Języki publikacji: EN

Abstrakty

EN

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 tooperate, which imposes great uncertainty on the food quality assurance model. To handlethe uncertainty, a support vector machine (SVM) classifier algorithm is developed todeal with the failure sensor effect using a data imputation strategy. The proposed modelis evaluated experimentally by means of benchmark datasets, and validated in a real-time environment by programming an Arduino-UNO controller in the internet of things(IoT) environment.

Słowa kluczowe

EN

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

PL

e-nos; imputacja danych; Zapewnienie jakości; klasyfikacja wieloklasowa MWN; metoda k najbliższych sąsiadów; Internet rzeczy; Arduino UNO

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2022
• Tom: Vol. 29, no. 1-2 spec.
• Strony: 105--123
• Opis fizyczny: Bibliogr. 22 poz., il., tab., wykr.

Twórcy

autor

Deepa S. N.

• Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh, India

autor

Jayalakshmi N. Yogamba

• Department of Electrical and Electronics Engineering, Dr. Mahalingham College of Engineering and Technology, Pollachi, Coimbatore, Tamil Nadu, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).