On Mining Rating Dependencies in Online Collaborative Rating Networks

A STAR Public Sector RD, Singapor

SKEWNESS AND PERMUTATION

The skewness criterion of phylogenetic structure in data is too sensitive to character state frequencies, is not sensitive enough to number of characters (degree of corroboration) and relies on counts of arbitrarily-resolved bifurcating trees. For these reasons it can give misleading results. Permutation tests lack those drawbacks and can be performed quickly by using approximate parsimony calculations, but the test based on minimal tree length can imply strong structure in ambiguous data. A more satisfactory test is obtained by using a support measure which takes multiple trees into account.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73640/1/j.1096-0031.1992.tb00071.x.pd

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques