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 …

The current development of the satisfiability logical representation in Discrete Hopfield
Neural Network has two prominent perspectives which are systematic and non-systematic …

Satisfiability is prominent in the field of computer science and mathematics because SAT
provides an alternative to represent the knowledge of any datasets. Fueled by this nature …

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

In the perspective of logic mining, the attribute selection, and the objective function of the
best logic is the two main factors that identifies the effectiveness of our proposed logic …

Amazon. com Inc. seeks alternative ways to improve manual transactions system of granting
employees resources access in the field of data science. The work constructs a modified …

Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield
Neural Network models ensures optimal synaptic weight management, which consequently …

Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm,
inspired by the presidential election model conducted globally. In this research, we will …

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 …

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 …