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We introduce a novel way of measuring the entropy of a set of values undergoing changes.
The region complexity is estimated by means of the entropy of the gray-level information; shape information is obtained by measuring the entropy of significant orientations.
Their proposed performance measurement model used the multi-criteria optimization and compromise solution method (Opricovic 1998) combined with measuring the entropy of the weights of the criteria.
Hoping to develop an absolute measure, biophysicist Steven Strong and colleagues at the NEC Research Institute in Princeton, New Jersey, hit on the idea of measuring the entropy or "disorder" of the electrical pulses passed on by the neuron.
One of the most widely used approaches relevant to information loss is based on measuring the entropy of data after anonymization by calculating the tuples fraction in each block of anonymized data, as in Eq. 6.
A biologically meaningful complexity measure has been proposed by measuring the entropy over multiple time scales inherent in physiologic signals, termed multiscale entropy (MSE) [44].
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We measure the entropy as a function of the phase-space of the values, i.e. their magnitude and velocity of change, using a method based on the abstract measure of entropy introduced by the philosopher Rudolf Carnap.
To begin with, they measured the entropy estimations of a given executable and change over the entropy estimations of a specific area of memory into typical representations.
This is implemented through the Gini impurity, a variant of the Gini index, which measures the entropy of the output properties of the instances [50].
Thus we measure the entropy of the group as H ( g ) = − ∑ t ∈ T ( g ) p ( t ) ⋅ log 2 p ( t ), (8).
In this section, we briefly repeat the method to measure the entropy of arbitrary undirected and connected networks, see [28].
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