Sentence examples for accuracy classify from inspiring English sources

We further identified the most significant DMRs (n = 57) that could, with 89% accuracy, classify the malignant samples, representing potential OC DNA methylation biomarkers.

MethPed could, with a high accuracy, classify many of these tumors as GBMs, ependymomas, or one of the medulloblastoma subgroups, demonstrating the benefit of using the MethPed classifier for identifying more likely diagnoses (Table  2).> Stratification of patients with pediatric tumors with differing biological behavior or responsiveness to specific therapies is urgently needed.

We achieved 100% of accuracy classifying the blobs from the annotated subset (second and third column of Table 1) as either abandoned or removed.

Comparing the recognition rates observed for one-to-one composer classification tasks, one can see the robustness of our proposed method: the lowest reported accuracy is at 86.1% for a Chopin vs. Beethoven task compared with a 63.8% reported in [20], while the best performance marked a 99.5% accuracy classifying Vivaldi vs. Joplin.

We demonstrate that the transcriptional response to food intake is robust by constructing a classifier from the gene expression traits with >90% accuracy classifying individuals as being in the fasted or fed state.

Table 3 Performance of classifiers on unseen P2P botnets   Decision trees Random forests Bayesian network   Classified Classified Accuracy Classified Classified Accuracy Classified Classified Accuracy   malicious benign   malicious benign   malicious benign   Zeus 2,696 55 98 % 2,717 34 98.76 % 2,660 91 96.69Nugache 42 7 85.71% 43 6 87.76% 48 1 97.96%.

Both user's and producer's accuracy to classify beetle infested stands increased over the temporal sequence of image dates.

The experimental results show that the accuracy to classify the diabetes samples can be up to 68.66%.

An application of breast cancer MRI imaging has been chosen and hybridization system has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: Benign or Malignant.

We subsequently tried to leverage such user specific features along with accuracy to classify users as legitimate or attacker.

By using machine learning techniques and their proposed method, they achieved up to 90%% accuracy to classify life facets/type of relation in contact (family, work, social).