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Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model

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dc.contributor.author Kaya, Aydın
dc.contributor.author Keçeli, Ali Seydi
dc.contributor.author Çatal, Çağatay
dc.contributor.author Tekinerdoğan, Bedri
dc.date.accessioned 2021-06-10T11:33:41Z
dc.date.available 2021-06-10T11:33:41Z
dc.date.issued 2020-06
dc.identifier.citation Kaya, Aydın...et al (2020). "Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model", Sensors, Vol. 20, No. 11. tr_TR
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/20.500.12416/4758
dc.description.abstract For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general. tr_TR
dc.language.iso eng tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Classifier tr_TR
dc.subject Single Plurality Voting System tr_TR
dc.subject Ensemble Classifier tr_TR
dc.subject Machine Learning tr_TR
dc.subject Beef Cut Quality Prediction tr_TR
dc.title Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model tr_TR
dc.type article tr_TR
dc.relation.journal Sensors tr_TR
dc.contributor.authorID 3530 tr_TR
dc.identifier.volume 20 tr_TR
dc.identifier.issue 11 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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