The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform
Paulo Henrique Goncalves Dias Diniz - One of the best experts on this subject based on the ideXlab platform.
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quantification and identification of adulteration in the fat content of chicken hamburgers using digital images and chemometric tools
Lwt - Food Science and Technology, 2019Co-Authors: David Douglas De Sousa Fernandes, Florencia Romeo, Gabriela Krepper, Maria Susana Di Nezio, Marcelo Fabian Pistonesi, Maria Eugenia Centurion, Mario Cesar Ugulino De Araujo, Paulo Henrique Goncalves Dias DinizAbstract:Abstract In this work, we developed an eco-friendly methodology for quantification and identification of adulteration in the fat content of chicken hamburgers by combining color histograms (in RGB, HSI, and Grayscale channels) obtained from digital images and chemometric tools. For this, 74 Samples of chicken hamburgers with a fat content of 14.27–47.55% (w w−1) were studied, taking into account adulterations with a fat content higher than 20% (w w−1), as limited by Argentinean legislation. In both quantitative and qualitative approaches, chemometric models containing HSI histograms achieved the best results, because this is very suitable in situations where there is a need to separate the chromaticity from the intensity. In other words, the opacity of the Sample surfaces increases with increasing fat content. PLS/HSI achieved the best quantification result with a R2 of 0.95, RMSEP of 2.01% w w−1, REP of 7.26% w w−1 and RPD of 4.47 in the prediction set, while SPA-LDA/Grayscale + HSI reached the most satisfactory in the test set with only one Misclassified Sample. Therefore, the proposed methodologies represent excellent alternatives to conventional Soxhlet extraction method, since they follow the primary principles of Green Analytical Chemistry, avoiding waste generation, besides not using either chemical reagents or solvents.
Muhammad Ahmad - One of the best experts on this subject based on the ideXlab platform.
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Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Muhammad AhmadAbstract:Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new Samples. Second, very limited research has been carried out on joint spectral-spatial information and finally, a minor but still worth mentioning is the stopping criteria which not being much considered by the community. Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) method. We first investigate a spatial prior fuzziness-based Misclassified Sample information. We then compute the total local and global for both within and between class information and formulate it in a fine-grained manner. Later this information is fed to a discriminative objective function to query the heterogeneous Samples which eliminate the randomness among the training Samples. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL classifiers.
Ahmad Muhammad - One of the best experts on this subject based on the ideXlab platform.
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Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
2020Co-Authors: Ahmad MuhammadAbstract:Traditional Active/Self/Interactive Learning for Hyperspectral Image Classification (HSIC) increases the size of the training set without considering the class scatters and randomness among the existing and new Samples. Second, very limited research has been carried out on joint spectral-spatial information and finally, a minor but still worth mentioning is the stopping criteria which not being much considered by the community. Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) method. We first investigate a spatial prior fuzziness-based Misclassified Sample information. We then compute the total local and global for both within and between class information and formulate it in a fine-grained manner. Later this information is fed to a discriminative objective function to query the heterogeneous Samples which eliminate the randomness among the training Samples. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL classifiers.Comment: 13 pages, 7 figure
David Douglas De Sousa Fernandes - One of the best experts on this subject based on the ideXlab platform.
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quantification and identification of adulteration in the fat content of chicken hamburgers using digital images and chemometric tools
Lwt - Food Science and Technology, 2019Co-Authors: David Douglas De Sousa Fernandes, Florencia Romeo, Gabriela Krepper, Maria Susana Di Nezio, Marcelo Fabian Pistonesi, Maria Eugenia Centurion, Mario Cesar Ugulino De Araujo, Paulo Henrique Goncalves Dias DinizAbstract:Abstract In this work, we developed an eco-friendly methodology for quantification and identification of adulteration in the fat content of chicken hamburgers by combining color histograms (in RGB, HSI, and Grayscale channels) obtained from digital images and chemometric tools. For this, 74 Samples of chicken hamburgers with a fat content of 14.27–47.55% (w w−1) were studied, taking into account adulterations with a fat content higher than 20% (w w−1), as limited by Argentinean legislation. In both quantitative and qualitative approaches, chemometric models containing HSI histograms achieved the best results, because this is very suitable in situations where there is a need to separate the chromaticity from the intensity. In other words, the opacity of the Sample surfaces increases with increasing fat content. PLS/HSI achieved the best quantification result with a R2 of 0.95, RMSEP of 2.01% w w−1, REP of 7.26% w w−1 and RPD of 4.47 in the prediction set, while SPA-LDA/Grayscale + HSI reached the most satisfactory in the test set with only one Misclassified Sample. Therefore, the proposed methodologies represent excellent alternatives to conventional Soxhlet extraction method, since they follow the primary principles of Green Analytical Chemistry, avoiding waste generation, besides not using either chemical reagents or solvents.
Donato Impedovo - One of the best experts on this subject based on the ideXlab platform.
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About retraining rule in multi–expert intelligent system for semi–supervised learning using SVM classifiers
International Journal of Signal and Imaging Systems Engineering, 2014Co-Authors: Donato Barbuzzi, Giuseppe Pirlo, Donato ImpedovoAbstract:This paper proposes three methods in order to retrain classifiers in a multi–expert scenario, when new (unknown) data are available. In fact, when a multi–expert system is adopted, the collective behaviour of classifiers can be used for both recognition aims and selection of the most profitable Samples for system retraining. More specifically a Misclassified Sample for a particular expert can be used to update the expert itself if the collective behaviour of the multi–expert system allows the classification of the Sample with high confidence. In addition, this paper provides a comparison between the new approach and those available in the literature for semi–supervised learning using the SVM classifier by taking into account four different combination techniques at abstract and measurement levels. The experimental results, which have been obtained using the handwritten digits of the CEDAR database, demonstrate the effectiveness of the proposed approach.
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ISSPA - Supervised learning strategies in multi-classifier systems
2012 11th International Conference on Information Science Signal Processing and their Applications (ISSPA), 2012Co-Authors: Donato Impedovo, Giuseppe Pirlo, Donato BarbuzziAbstract:This paper presents three strategies in order to re-train classifiers in a multi-expert scenario when new labeled data become available. The simplest possibility is the use of the entire new dataset. The second possibility is related to the consideration that each single classifier is able to select new patterns starting from those on which it performs a miss-classification. Finally, the multi expert system behavior can be inspected to select profitable Samples. More specifically a Misclassified Sample, for a particular classifier, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. The three approaches are compared under different conditions on two different state of the art performing classifiers by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training Samples, as well as by the specific combination decision schema.
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ICDAR - Updating Knowledge in Feedback-Based Multi-classifier Systems
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Donato Impedovo, Giuseppe PirloAbstract:In pattern recognition tasks it is frequent that new (labeled) data became available as the specific application scenario evolves. When a multi expert system (ME) is adopted, the collective behavior of classifiers can be used to select the most profitable Samples in order to update the knowledge base. More specifically a Misclassified Sample, for a particular classifier, is used to update that classifier only if that Sample produces a misclassification by the ensemble of classifiers. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific Samples are selected by the individual classifier. Successful results have been obtained by considering the CEDAR (handwritten digit) database, moreover it is also shown how they depend by the specific combination decision schema, as well as by data distribution.