Current studies on non-systematic satisfiability in Discrete Hopfield Neural Network are able
to avoid production of repetitive final neuron states which improves the quality of global …

Abstract The existing Discrete Hopfield Neural Network with systematic Satisfiability models
produced repetition of final neuron states which promotes to overfitting global minima …

Computing paradigm based on von Neuman architectures cannot keep up with the ever-
increasing data growth (also called “data deluge gap”). This has resulted in investigating …

Hopfield networks are artificial neural networks which store memory patterns on the states of
their neurons by choosing recurrent connection weights and update rules such that the …

Existing satisfiability (SAT) is composed of a systematic logical structure with definite literals
in a set of clauses. The key problem of the existing SAT is the lack of interpretability of a …

Methods for time series prediction and classification of gene regulatory networks (GRNs)
from gene expression data have been treated separately so far. The recent emergence of …

This study proposes a novel hybrid computational approach that integrates the artificial
dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal …

The architecture of communication within the brain, represented by the human connectome,
has gained a paramount role in the neuroscience community. Several features of this …

Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating
patterns. In this study, we present a novel storage method that harnesses naturally-occurring …

Recently, a variety of non-systematic satisfiability studies on Discrete Hopfield Neural
Networks have been introduced to overcome a lack of interpretation. Although a flexible …