*2.3. Min-Max Normalization*

Normalization linearly transforms variables within specific ranges based on the minimum and maximum median absolute deviations of the variable values, avoiding changes to priorities in the variables because of the scale. Equation (1) represents the standard deviation required in the transformation as *Xstd*, and Equation (2) indicates the variable scaling [46,47].

$$X\_{std} = \frac{\mathbf{x} - X\_{\text{min}}}{X\_{\text{max}} - X\_{\text{min}}} \tag{1}$$

$$X\_{scaled} = X\_{std} \times (X\_{max} - X\_{min}) + X\_{min} \tag{2}$$

where *Xscaled* is the new value transformed from the original value *x* ∈ *X* and *Xmax* and *Xmin* are the maximum and minimum values, respectively.
