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Building a reliable effort estimation model usually requires historical data.
The large tear such as this model usually requires many sutures.
Thus, a full coupling matrix model usually requires too many parameters to be estimated.
Training an ML model usually requires sizable amounts of labeled ground-truth data typically obtained via laborious manual chart reviews by expert clinicians.
The reason is that the LBF model is sensitive to the initial contour, and the model usually requires setting different initial contours for the disconnected objects.
The generating of a model usually requires the inputs of an analyst, and because the science changes continually, analysts should have their training updated regularly.
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In addition, the detailed rules implemented in a micro-simulation model usually require an accurate calibration process, aimed at replicating the actual driving in traffic.
However, building of ANN surrogate models usually requires large training datasets.
Complex constructive solid modeling usually requires very fast and expensive special purpose hardware, which is not widely available.
The definition of such models usually requires the execution of various experimental tests for the determination of the associated material parameters, constituting a significant cost/effort in terms of characterization.
Most inexpensive portable models usually require that you update the GPS device using a computer.
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