Sentence examples for attributes for predicting from inspiring English sources

The present study is a longitudinal research which uses baseline individual attributes for predicting three measures of individual resilience in two consecutive periods of terror attacks.

We derived 24 predictor variables from Landsat 8 OLI satellite imagery in order to assess the use of a wide range of spectral and textural attributes for predicting AGB (Table 3).

While numerous studies have demonstrated the importance of terrain attributes for predicting landscape-scale soil variability, considerable uncertainty exists as to the scale-dependency of light detection and ranging (LiDAR) derived terrain attributes on the accuracy of soil-landscape model predictions.

Historically, patient- and tumor-related factors such as age, histology, tumor volume and stage have been critical attributes for predicting patient outcome and overall survival for cervical cancer.

We used as predictor attributes only biological process (BP) GO terms, which are more easily interpretable as attributes for predicting whether a DNA repair gene is ageing related or not.

Although gamma ray was not the primary attribute for predicting TOC, the relationship between lithology (gamma ray) and TOC has been discussed by several authors (e.g., Sondergeld et al. 2010).

Because of the dependence of TOC computation on ILD, the computed TOC log motif is similar to that of ILD (Fig. 5), and the most significant attribute for predicting TOC is also Q2.

The reason Q2 is the primary attribute for predicting TOC can be found in the equation used in TOC computation (Passey et al. 1990): TOC wt % = Δ Log R 10 2.2 9 7 - 0. 1 6 8 8 LOM, (5 where Δ Log R is the separation between the sonic log (DT) and resistivity (ILD) (Fig. 5).

Detailed information on these 53 attributes is provided in Additional File 1: Appendices 1 to 3. GA was used to rank and select attributes that are useful for predicting patients' rechallenge status. Figure 1 shows an overview of the GA attribute selection process.

For predicting attributes such as weight, our method relies on vessel type classification.

SVM classifier employs the generic representation learnt for vessel type classification, whereas CNN employs a representation specifically learnt for predicting attributes.