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Consequently, this matrix was normalized with the quantile method.

The total dimensions relation matrix was normalized by Eq. (10).

elements derived from a Gaussian distribution N 0,1), and each row in the matrix was normalized to a unit magnitude.

Each column of this matrix was normalized using the standard deviation of the data in that column.

First, where the PSSM matrix was normalized

To generate input vectors of fixed length for SVM training, the PSSM matrix was normalized

The resulting matrix is normalized to fit to physical constraints.

The direct-influential matrix is normalized using Eq. (4) and is shown in Table 4.

To make the performance indices dimensionless and comparable, the decision matrix is normalized.

To compare the activity vectors, the columns of the matrix are normalized as shown in Fig. 6.

In this model, first of all, the average of six metrics (criteria) in all problems is calculated then this matrix is normalized.