Difference between revisions of "DT's page for Kahn Food project"
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=Food Classification= | =Food Classification= | ||
| − | * [http://www.sciencedirect.com/science/article/pii/S0003267011008622 Application of artificial neural network in food classification]. | + | <br /> |
| + | Some results from the Google scholar search on [https://scholar.google.com/scholar?q=food+classification+neural+network&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwjghZ_TjfjXAhWENiYKHVhyD58QgQMIJTAA "food classification neural network"] | ||
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| + | * Beer classification: [http://www.sciencedirect.com/science/article/pii/S0003267011008622 Application of artificial neural network in food classification]. | ||
<blockquote>Artificial neural network (ANN) classifiers have been successfully implemented for various quality inspection and grading tasks of diverse food products. ANN are very good pattern classifiers because of their ability to learn patterns that are not linearly separable and concepts dealing with uncertainty, noise and random events. In this research, the ANN was used to build the classification model based on the relevant features of beer. Samples of the same brand of beer but with varying manufacturing dates, originating from miscellaneous manufacturing lots, have been represented in the multidimensional space by data vectors, which was an assembly of 12 features (% of alcohol, pH, % of CO2 etc.). The classification has been performed for two subsets, the first that included samples of good quality beer and the other containing samples of unsatisfactory quality. ANN techniques allowed the discrimination between qualities of beer samples with up to 100% of correct classifications. | <blockquote>Artificial neural network (ANN) classifiers have been successfully implemented for various quality inspection and grading tasks of diverse food products. ANN are very good pattern classifiers because of their ability to learn patterns that are not linearly separable and concepts dealing with uncertainty, noise and random events. In this research, the ANN was used to build the classification model based on the relevant features of beer. Samples of the same brand of beer but with varying manufacturing dates, originating from miscellaneous manufacturing lots, have been represented in the multidimensional space by data vectors, which was an assembly of 12 features (% of alcohol, pH, % of CO2 etc.). The classification has been performed for two subsets, the first that included samples of good quality beer and the other containing samples of unsatisfactory quality. ANN techniques allowed the discrimination between qualities of beer samples with up to 100% of correct classifications. | ||
</blockquote> | </blockquote> | ||
| + | * NN used for drug/nondrug classification | ||
| + | * NN used for the classification of honey | ||
