Sentence examples for various combinations of methods from inspiring English sources

These strategies were deployed using various combinations of methods for calculating parameters that describe the shape of FDCs (polynomial expressions and probability distribution functions) and then methods for estimating these parameters at ungauged sites using available catchment characteristics (stepwise linear regression and random forests).

Researchers unfamiliar with phylogenetic analysis are perplexed by selecting and installing programs, transferring files between programs, setting parameters for the complete process, running iterations with various combinations of methods and parameters [2], as well as recalling all tools and parameter-related choices made during analysis.

Various combinations of methods are also used in glycan isolation and characterization as summarized in recent articles [ 62- 72].

The aim of this paper is to evaluate the performance of regressive and autoregressive methods as well as various combinations of regressive/autoregressive methods for denoising pressure and pressure derivative transient data in comparison to the wavelet transform method which has widely been used in the petroleum industry.

Then, through an iterative process, various combinations of components, methods and strategies are implemented/improved and tested.

Various combinations of geophysical methods have been used with an accompanying increase in the degree of accuracy in the location of suitable groundwater reservoir.

We have applied a robust ranking approach utilized by Gupta et al. [43], to study the relative performances of various combinations of feature selection methods with the proposed feature extraction method, i.e., FEWT with respect to EWT.

The PES scans were performed using various combinations of DFT/coupled-cluster methods and basis sets, and the effect of bulk solvent was examined by using the integral equation formalism of the polarizable continuum model (IEF PCM).

The MAE and RMSE were very similar across the various combinations of models and methods, with Model 2 (using I-QOL subscale scores as key explanatory variables) based on GLM estimator having the lowest MAE (0.1461) and lowest RMSE (0.1795).

We also test the performance of various combinations of the different smoothing methods to filter the same noisy data.

We also conducted experiments on various combinations of gene-expression data generation methods and network inference methods.