Sentence examples for segment classification from inspiring English sources

Objective segment classification as normal or abnormal was achieved by comparison to prediction intervals of FA and mean diffusivity (MD) defined in normal subjects.

In the clustering process, different time segment classification results may be different, a certain group of goods may increase or decrease in the next period of time, corresponding to the product that is newly added to the shopping cart or abandoned during the process of shopping.

If there was a mismatch, the current segment classification was changed to the appropriate segment to follow the previous one, provided that the measured distance was within the threshold of the new identity's reference distance in the database, otherwise the classification identity of the current segment was discarded.

Using Endler's [58] segment classification method, we derived objective estimates of hue (0 360°: 0° = red; 60° = yellow), chroma (0 100%), and brightness (0 100%), and we used the mean colouration of the three body parts measured in the analyses.

Protocol for calculating segment classification color variables.

Quantifying and comparing colors with the segment classification method begins with the measurement of reflectance spectra.

To that end we've compiled a list of 19 new vehicles among a broad range of size, price, and market segment classifications that quantifiable data predicts will retain the highest percentages of their original value at trade-in time, afford the highest levels of occupant protection in a crash, and can be expected to do the best job of helping their drivers avoid getting into collisions altogether.

1) Input the network structure, use the method in Section 3.4.3, automatically traversing feeder segments classification of all non-faulty components by computer, establish FMEA table.

Coronary artery segments were classified according to the 15-segment AHA classification [ 14].

In this process, the datasets are first divided into small segments, and classification algorithms are applied on an increasing number of segments until the identification score reaches a pre-defined threshold of satisfaction (ideally 100%).

For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%.