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It can be observed that only the Euclidean and Manhattan distances achieved more than 50% of classification for each class, whereas the Euclidean distance achieved a maximum of 63.75% and the Manhattan distance a maximum of 60%. Table 9 Confusion matrix of the classification means for the 4-MHz Ponatinib nmr frequency pulse echo signals at 950 ��C. The data in Table 10 show that for the 5-MHz frequency backscattered signals, a poor performance was achieved in general. The Manhattan distance achieved a maximum of 45% and a minimum of 31.25% accuracy, whereas the Euclidean distance achieved a maximum and a minimum of 37.5% and 36.25%, respectively; the other ones kept below 35%. Table 10 Confusion matrix of the classification means for the 5-MHz frequency backscattered signals at 950 ��C. Table 11 shows the confusion matrix of the classification means of the 5-MHz frequency pulse echo signals. The data presented show that the best performance for the temperature of 950 ��C was achieved by the Euclidean distance with a maximum of 70%, followed by the Manhattan distance with 68.75%; the others remained between 57.5% and 22.5%. Table 11 Confusion matrix of the classification means for the 5-MHz frequency pulse echo signals at 950 ��C. In Table 12 is shown the training and testing average times in milliseconds for the 4- and 5-MHz frequency signals. All distances achieved analogous results for the training and testing times for the backscattered signals, keeping a time of 0.2 ms for training and of 0.1 for testing. For the pulse echo signals, the average times range from 0.2�C0.4 ms for training and 0.1 for testing. Table 12 Average of training and testing times in milliseconds for the 4- and 5-MHz frequency signals at 950 ��C. 3.1.3. Samples Aged at 650 and 950 ��C For this dataset, as can be seen in Table 13, the best accuracy was achieved by the Euclidean distance, with a value of 65.86%, but the highest harmonic mean belongs to the Manhattan metric, with 83.5%. This is due to the fact that in some rounds, the classification performance achieved by the Manhattan distance was higher than the one achieved by the Euclidean distance. The best classification of the Manhattan distance was 71.4%, whereas the best accuracy achieved by the Euclidean distance was equal to 67.86%. The other distances achieved accuracy rates lower than 30%, with the chi-squared distance achieving in its worst classification a value of 10.71%. Table 13 Accuracy rates and harmonic mean for the 4- and 5-MHz frequency signals at 650 and 950 ��C. Regarding the processing times, indicated in Table 14, the distances that classified the samples correctly more often were those that took longer to do the training, with the Manhattan distance taking between 0.5 and 1 ms and the Euclidean distance from 0.4�C0.6 ms. For the test, the Manhattan distance took from 0.16�C0.3 ms, whereas the Euclidean distance took from 0.18�C0.24 ms.