Most of the optimization problems encountered in the real world are discrete type which
involves decision variables defined in the discrete search space. Binary optimization …

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 …

Creating optimal logic mining is strongly dependent on how the learning data are structured.
Without optimal data structure, intelligence systems integrated into logic mining, such as an …

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

Recently, new variants of non-systematic satisfiability logic were proposed to govern
Discrete Hopfield Neural Network. This new variant of satisfiability logical rule will provide …

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 …

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 …

Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network
(ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology …