A researcher implements a feature engineering methodology to extract features they believe will be useful; however, this is ultimately an educated guess.
In the first step, the researcher implements a selection model (typically using binary logistic or binary probit regression) using treatment status as the dependent variable and variables that explain selection into the treatment or control group as predictors.
The researcher implementing the exercise intervention will have training provided by an experienced physiotherapist (KH), and will use an exercise prescription algorithm developed for the study.
Following the baseline assessment, the next consecutive envelope will be opened by a researcher not involved in the assessments, and the group allocation will be conveyed to the researcher implementing the intervention activities.
The researchers implemented the logging systems with OpenStack and Snort.
The researchers implemented the model in OpenStack project.
The researchers implemented the model using OpenNabula platform and the Xplico analysis for validation.
Recently, researchers implemented sparse component analysis (SCA) to improve the method of blind image separation [19, 20].
The researchers implemented a learning algorithm based on central pattern generators with an optimization method, which was able to generate successful crawling forward motions.
The researchers implemented the proposed model in a proof of concept prototype on a client and server applications that consist of a windows service [33].
