The phrase "adapt the input" is correct and usable in written English.
It can be used when discussing the modification or adjustment of data or information to fit a specific context or requirement.
Example: "In order to improve the model's performance, we need to adapt the input to better reflect the current trends."
Alternatives: "modify the input" or "adjust the input".
RUSLE has been modified from the Universal Soil Loss Equation (USLE) by adapting the input factors to the local conditions (McCool et al. 1995; Renard et al. 1996).
An extension to PESQ was developed that adapted the input filters and MOS mapping to allow wideband signal quality prediction [3].
We believe that this is not only because of the particularities of our algorithms, but also because we are adapting the input parameters to different levels of noise.
Nonetheless, the algorithms alone, even after adapting the input parameters on a participant basis, are always subject to errors, particularly when processing low-quality data.
For this reason, it can also be argued that adapting the input parameters is a step in reducing the levels of noise for each participant's results.
We believe that adapting the input parameters on a participant basis can avoid the detection of artifactual fixations and will lead to more accurate and reliable experimental results.
The ultimate aim of adapting the input parameters and applying certain post hoc validation criteria is to obtain the most accurate outcome by the algorithms alone and, thus, reduce the hand-coding time during the subsequent step.
Although the use of different velocity thresholds can lead to different outcomes from the detection algorithms, it is possible to remove a number of artifactual fixations (this is especially important in noisy data sets) by also adapting the input parameters for the post hoc validation criteria (explained below) and manually fixing the fixations that were not detected correctly.
These algorithms already highlighted the importance of adapting the input parameters to different levels of noise but still did not solve the problem of accurately detecting fixations in data sets with relatively higher levels or noise, such as those from infants.
