Sentence examples for behavior generated from from inspiring English sources

There are dozens of credit scores available to lenders and financial institutions that are based on consumer behavior generated from their credit reports.

We propose the name "monoosteophil" to distinguish them from the three known types of bone cells and to emphasize their bone loving behavior generated from monocytes.

Because of the greater weight they place on biological accuracy, CCN models differ substantially from traditional neural network models in how each individual unit is modeled, how learning is modeled, and how behavior is generated from the network.

Different complex, global behaviors are generated from simple individual behaviors.

A general SP simulation model was initiated, in which polymer viscosity dependence on concentration and salinity was established in the laboratory; surfactant phase behavior parameters were generated from test-tube results; and oil desaturation was based on additional coreflooding.

The results show that layout significantly affects online consumer behavior, but that predictions generated from the literature of conventional retailing about differences in the outcome of layouts do not generally hold in a virtual setting.

For instance, it is very difficult to model the behavior of traffic being generated from a massive amount of devices in parallel, and also it becomes more difficult in the presence of strong spatial and temporal correlation between devices [25].

The emission probabilities of HMM used in CLImAT give comprehensive description of the statistical behavior of sequencing data generated from tumor samples.

2. Assess current behaviors and progress – the interviewer summarizes the participants' behavior based on a participant profile generated from the questionnaire or survey and checks whether this profile fits their current behavior.

This information is generated from mobility behavior, gathered in the survey "Österreich Unterwegs 2013/2014" [16].

This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points.