To compare the results of the proposed algorithm with those of other methods, we implemented the following algorithms: Algorithm A: Hierarchical system for objectionable video detection [12].
Section 2.6 describes the comparison methods we implemented in this study.
Methods: We implemented a program assessing internal radiation contamination using a whole body counter (WBC) measurement and a questionnaire survey in Minamisoma, between October 2011 and March 2012.
In addition to the above clustering methods, we implemented the following tree-based clustering algorithms: single linkage hierarchical clustering – HCL [ 23, 24], neighbour joining-NJ [ 25], and unweighted pair group method with arithmetic mean – UPGMA [ 26].
To demonstrate the methods we implemented the local and global Q statistics for clustering of residential histories, specifically the local test at time t, Q i, k, t (Equations 6), and its global counterpart Q k, t (Equation 10).
Consequently, to compare the predictive capability of our approach against other methods, we implemented prognostic models that differ in the way class-specific transcriptional association networks are inferred, and in the supervised classification methodology applied on the network information.
To verify the effectiveness of our method, we implement our method in Matlab and conduct experiments on it.
To show the efficiency of the proposed method, we implement it on three nonlinear singular boundary problems that arise in real physiology applications.
For each method we implement a shared, adaptive time-step criterion as in equation (1), with a time-step parameter (eta= 2^{-7}).
