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 …

Mining the best logical rule from the data is a challenging task because not all attribute of the
dataset will contribute towards the optimal logical representation. Even if the correct …

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

A conventional systematic satisfiability logic suffers from a nonflexible logical structure that
leads to a lack of interpretation. To resolve this problem, the advantage of introducing …

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 …

One of the main problems in representing information in the form of nonsystematic logic is
the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic …

RBFNN with different algorithms and the logic mining method for forecasting constitute the
most significant tools and techniques, which are used to demonstrate the economic growth …

The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent
on its network structure. Thus, the output of ANNs has long suffered from a lack of …

The k Satisfiability logic representation (kSAT) contains valuable information that can be
represented in terms of variables. This paper investigates the use of a particular non …

An effective recruitment evaluation plays an important role in the success of companies,
industries and institutions. In order to obtain insight on the relationship between factors …