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This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score.
As a result, a user would receive the same anomaly score regardless of the local network in which they are being investigated.
The total anomaly score TAS is computed by considering all broken rules as well as the delay since last anomaly for a given rule.
After evaluating the anomaly score of the test sequence S q, a complementary test is required to check whether it significantly deviates from the score of other sequences to characterise an actual anomaly.
For the specific case of semi-supervised learning, the three scenarios can be formulated as follows [24]: Formulation 1: Given a set of n sequences, (mathbb {S} = {S_{1}, S_{2},..., S_{n}}), and a sequence S q belonging to a test data set S, compute an anomaly score for S q with respect to the training data set (mathbb {S}).
In this case, the anomaly score is calculated from the logarithm of the raw BLASTX score, hence better BLASTX matches get a higher score.
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Then, based on the learned features, multiple one-class SVM models are used to predict the anomaly scores of each input.
The sum of the anomaly scores for each region is used to prioritize curation.
In patients with non-neurological primary disease, the use of alternative observables, such as pupillary anomaly, RASS score and sedative use, may serve as a substitute score in cases of missing or unobservable GCS assessments.
Based on this we set the anomaly detection score z t to 2 because of its highest precision and recall.
The scores are generally set in the range 1 10, but there is nothing to prevent any value being assigned to an anomaly's score.
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