The nature of statistical learning theory
The nature of statistical learning theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Integrating multispectral reflectance and fluorescence imaging for defect detection on apples
Computers and Electronics in Agriculture
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Aspergillus flavus (A. flavus) produces secondary metabolites, aflatoxins, that are harmful to both humans and animals. Because of stringent federal regulation requirements as well as the limitations of available detection methods, there is an urgent need for rapid, non-invasive and effective techniques such as hyperspectral imaging, for the detection of the toxigenic strains of A. flavus. Hyperspectral images of toxigenic and atoxigenic strains of A. flavus were classified. Principal component analysis (PCA) was applied for data decorrelation and dimensionality reduction. A Genetic Algorithm (GA) was implemented for the selection of principal components (PCs) based on Bhattacharya Distance (B-Distance). A Support Vector Machine (SVM) was successfully applied for the classification. Under halogen light sources, in average 83% of the toxigenic fungus pixels and 74% of the atoxigenic fungus pixels were correctly classified; while under UV light sources, 67% of the toxigenic fungus pixels and 85% of the atoxigenic fungus pixels were correctly classified. The pair-wise classification accuracies between toxigenic AF13 and each atoxigenic fungus species (AF38, AF283 and AF2038) were 80%, 91% and 95% under halogen light sources, and 75%, 97% and 99% under UV lights, respectively.