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Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva |
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Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN |
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Journal Article |
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2023 |
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Food Control |
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FC |
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147 |
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109554 |
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Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. |
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Admin @ si @ MNJ2023 |
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3882 |
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Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; O. Rodriguez-Leor; H. Tizon; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Radial Artery Volume Changes After Administration Of Two Different Intra-arterial Drug Regimens. Assessment by Intravascular Ultrasound |
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2010 |
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Journal of the American College of Cardiology |
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JACC |
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56 |
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13s1 |
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B119 |
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BCNPCL @ bcnpcl @ SFC2010b |
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1364 |
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O. Rodriguez-Leor; Carlo Gatta; E Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri |
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Computationally Efficient Image-based IVUS Pullbacks Gating |
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2008 |
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European Heart Journal, ESC Supplement, Munich, 2008, p. 775 |
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BCNPCL @ bcnpcl @ RGF2008 |
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1036 |
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O. Rodriguez-Leor; E Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Misael Rosales; Petia Radeva |
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Empirical simulation model of intravascular ultrasound |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna (Austria) |
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BCNPCL @ bcnpcl @ RFM2003b |
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408 |
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O. Rodriguez-Leor; E Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva |
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Intravascular ultrasound segmentation using local binary patterns |
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2003 |
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European Heart Journal (IF: 5.997), ESC Congress 2003 |
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Vienna (Austria) |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RFM2003a |
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407 |
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