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We used Python and Grasshopper to integrate different simulation tools and implemented two methods of numeric optimization (gradient descent) and exhaustive search (parametric analysis) in order to validate the final result.
In our study three methods of tree learning were used: decision tree learning, where the dependent variable is discrete-values, and two methods of numeric prediction, regression and model trees.
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We establish a two-stage imputation mechanism in which we start with any method of numeric imputation and then form a granular representative of missing value.
A comparison between different types of numeric methods is made, to find the better option depending of the finality of the study.
A detailed clustering algorithm is developed on a basis of the standard FCM method and illustrated by means of numeric examples.
Interviews were identified by the date and location of the interview, logged in a simple string of numeric codes, a method that also ensured protection of interviewer and interviewee.
The performance of the proposed solution method has been examined against the hybrid genetic Taguchi algorithm (GATA) on a set of numeric instances, and results indicate that the proposed method can effectively provide better results than previous solution procedures.
Sensor fusion methods can principally be divided into grid-based (geometric) and parameter-based (numerical) approaches whereby in the case of numeric approaches.
In healthcare, this method can be used to dynamically gather online analyses of numeric data (eg, a specific dose of a drug for a specific disease) in clinical practice.
This method is proved to be valid and accurate by comparison of numeric and experimental data; the relative error is less than 2%.
The specific point would be that numeric and analytic methods of the theory of differential equations will have to be elaborated in a form preserving the prescribed algebroid configuration.
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