Sentence examples for model can deal from inspiring English sources

The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints.

The paper demonstrates by simulation how the proposed model can deal with issues such as scalability, efficiency and adaptability of resource discovery in future many-core systems which are the major challenges in the current state of the art.

The empirical evaluation of the classified genres from a repository of postings in an online course on earth science in a senior high school shows that GCS can effectively facilitate the coding process, and the proposed cascade model can deal with the imbalanced distribution nature of discussion postings.

Our results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the Face Detection Data Set and Benchmark (FDDB) [1] and the Annotated Facial Landmarks in the Wild (AFLW) [2] databases.

The model can deal with direct information (although in a very simple way) and witness information.

The RSF model can deal with intensity inhomogeneity accurately, but it is quite sensitive to contour initialization.

Among published grey models, the current non-equigap grey models can deal with data having unequal gaps, and have been applied in various fields.

Gene-by-gene models can deal better with variances that are gene-dependent (due to differences in gene expression levels).

Although Cox proportional hazards models can deal with both censored and complex cost distributions, irregular cost accrual may lead to bias in survival estimates.

The biological complexity of state-dependent strategies and phenotypic plasticity that dynamic programming models can deal with is generally out of reach for the other approaches.

Although the supervised chemogenomic models can deal with a number of targets simultaneously, they are also prone to the same model construction and evaluation challenges considered here, including model over-fitting because of issues related to, for instance, large feature space and selection bias.