… the entropy of a time series using the LogNNet neural network … introduced fluctuation-based
dispersion entropy to deal with … Letellier used recurrent plots to evaluate Shannon entropy in …

… neural network … Entropy based feature extraction is defined in two separate and groundbreaking
ways. In this work, the sample entropy, Shannon entropy and approximate entropy are …

… QRS analysis by spectral entropy Spectral entropy is based on Shannon entropy in … In
this paper, we consider Shannon’s entropy to describe the information in time-frequency …

… neural network and achieved 97.20% accuracy. Srinivasan et al. [Citation2] used time- and
frequency-domain features–recurrent neural network … and artificial neural network classifier to …

… Renyi entropy (RenEn) is a generalization of Shannon entropy, which quantifies diversity,
uncertainty or randomness of a system. The RenEn estimation rely on the parameter α, where …

… applied to neural network we have tested its performance assuming that classification results
… is more non-linear than behavior of error function based on the Shannon entropy ( q ≈ 1 ). …

… [13] extracted the L 2 norm, mean, Shannon entropy, and energy … entropy features for
BMAC vs. GMAC and BFMAC vs. DMAC classification tasks and the results of classification using …

… , the entropy is then computed using Shannon’s formula defined as: … based solely on the
structural entropy of a file. Second, the nature of the features learned by convolutional neural net…

… entropy and feed-forward neural network (FFNN). DWT … We use simple Shannon entropy
that basically employs only arithmetic and log operations. The method was implemented using …

… entropy has similar pattern with respect to the window size for surface air temperature and
rainfall, the rate of change of Shannon Entropy … the line diagrams depicting entropy for the two …