Sentence examples for development of such algorithms from inspiring English sources

Machine-learning algorithms can analyze large datasets and determine combinations of variables that consistently classify or predict a certain outcome.9 Such models have been widely applied in non-medical fields10 with nascent but promising use in medicine.11,12 Widespread adoption of EHRs has led to large collections of patient-level data being available for the development of such algorithms.

However, the lack of large datasets has hampered the development of such algorithms.

As panels of biomarkers complementary to CA125 are assembled, further development of such algorithms to take into account the combined profile will be required to ensure detection of OC at low volumes.

The availability of a standardized set of test data would allow comparison of different algorithms and procedures that operate on EMRs as well as provide a set of records for the development of such algorithms.

Here we present a fast and realistic sonar simulator enabling development and evaluation of such algorithms.We develop a classifier and then analyse its performances using our simulated synthetic sonar images.

This is especially important when standards for PQ measurement are in constant development, and the PQ issues in emerging smart grids will require tools for rapid development and implementation of such algorithms.

The application of such algorithms have led to the development of industrial microbial cell factories [ 3].

In addition to algorithmic development, much of her work has used concentration of measure tools and applied probability ideas to extend performance guarantees of such algorithms to the non-asymptotic regime.

That is a wild exaggeration, given what we know about the limitations of such algorithms.

The development of such an algorithm was motivated by difficulties in interpretation of the results of numerical experiments with the Sivashinsky equation using spectral methods.

The development of such an algorithm is necessitated by applications that require reduced spatial resolution, as is common in cartographic generalization, GIS applications, and geophysical modeling.