The accuracy of the NMR method is heavily dependent on the accuracy of the weight taken for reference compound as well as for the sample chosen.
To compute with certainty all the roots, the proposed method is heavily based on the knowledge of the total number of roots within the given interval.
The effect of this method is heavily dependent on the degree of dimension reduction; if it is considerable, useful information may be lost, and if it is less, clustering cannot be done effectively.
The HDGP-PC approach was robust in capturing the African, East Asian, and Native American components in the CA genomes, however, the self-eigen method was heavily influenced by a few outliers.
In addition, most of the robust methods are heavily dependent on selecting some weight filters which is not required in the proposed approach.
Estimates obtained through traditional two-dimensional counting methods are heavily biased by the counting parameters in relation to the size and shape of the neurons to be counted, resulting in a large range of inaccurate counts.
However, most of the cationic polymer based methods are heavily endosome centric.
Just as frequency-based methods are heavily dependent upon available mutation information, substitution-based algorithms are limited by available sequence and structure information.
Consequently data sets for the training of supervised methods are heavily unbalanced, and this could have a negative impact on the prediction accuracy of the classifier.
Current imaging analysis methods are heavily dependent on the experience of the pathologist, and they rely on subjective interpretations; introducing a parameter that is size irrelevant such as FD in conjunction with a learning system can prove to be extremely beneficial for improving both the time needed for a diagnosis and medical decisions.
