<p>Each row represents the actual variety and in which one was classified. Bolded values (diagonal of the matrix) are the number of samples properly classified. The last column shows the correctly classified percentage for each variety.</p><p>ANN: Artificial Neural Network; NoSNV+D: No application of Standard Normal Variate followed by De-trending; D2W5: Second-degree derivative and window size 5 Savitzky-Golay filter.</p><p>V: Viura; Gr: Grenache; T: Treixadura; Te: Tempranillo; S: Syrah; A: Albariño.</p><p>Confusion matrix from the global dataset execution with the best score (ANN, NoSNV+D, D2W5 and parameter set 6) with an overall correctly classified value of 77.08% (24 leaves per variety).</p
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p><b>A.</b> Classification result over all 21 neurons. Blue stars and red circle denote the classif...
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
<p>Each row represents the actual variety and in which one was classified. Bolded values (diagonal o...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>The rows hereby indicate the predicted, i.e. real class, whereas the columns indicate the actual ...
<p>Rows indicate the percentages of predicted syndromes for each of the syndromes in the study.</p><...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>(A), Confusion matrix of the model performance on the dataset of 15 scenes. The average accuracy ...
<p>The rows represent the true pollen types while the columns indicate how the images have been clas...
<p>Confusion matrix for the classifiers of RF, SVM, and WKNN using the input dataset with all the pr...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p><b>A.</b> Classification result over all 21 neurons. Blue stars and red circle denote the classif...
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
<p>Each row represents the actual variety and in which one was classified. Bolded values (diagonal o...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>The rows hereby indicate the predicted, i.e. real class, whereas the columns indicate the actual ...
<p>Rows indicate the percentages of predicted syndromes for each of the syndromes in the study.</p><...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>(A), Confusion matrix of the model performance on the dataset of 15 scenes. The average accuracy ...
<p>The rows represent the true pollen types while the columns indicate how the images have been clas...
<p>Confusion matrix for the classifiers of RF, SVM, and WKNN using the input dataset with all the pr...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
<p><b>A.</b> Classification result over all 21 neurons. Blue stars and red circle denote the classif...
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p