Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

Abstract

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to a Boolean formula.. Hopfield neural network (HNN) is one of the major type artificial neural network (NN) popularly known for it used in solving various optimization and decision problems based on its energy minimization machinism. The existing models that incorporate standalone network projected non-versatile framework as fundamental Hopfield type of neural network (HNN) employs random search in its training stages and sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) as a novel variant of probabilistic metaheuristic algorithm (MA) inspired by the behavior of real Ants, has been incorporated in the training phase of Hopfield types of the neural network (HNN) to accelerate the training process for Random Boolean kSatisfiability reverse analysis (RANkSATRA) based for logic mining. The proposed hybrid model has been evaluated according to robustness and accuracy of the induced logic obtained based on the agricultural soil fertility data set (ASFDS). Based on the experimental simulation results, it reveals that the ACO can effectively work with the Hopfield type of neural network (HNN) for Random 3 Satisfiability Reverse Analysis with 87.5 % classification accuracy

The effectiveness of big data classification control based on principal component analysis

“…On the other hand, dimension reduction using PCA, singular value decomposition (SVD), nonnegative matrix factorization (NMF), and sparse coding (SC) in image processing for the mammalian brain has been introduced [18]. Dimensionality reduction techniques, PCA, and NMF, have been used to explore the dependency of the standard PCA, NMF, ICA, and SVD algorithms on the selected number of dimensions [19].…”

The software requirements process for designing a microcontroller-based voice-controlled system

“…The DS1302 clock module keeps time without electricity. Smart desk lamps are more handy than standard workplace lighting [8], [26].…”

“…They are able to decipher the vocal orders because they are listening for them. The remote control in this scenario is a Bluetooth app that was installed on an android smartphone or tablet by the user [26]. The purpose of this paper is to outline all of the particulars and requirements that must be met in order to construct software that makes use of an Arduino microcontroller and an app that recognizes voices.…”

Deep Learning Applications and Their Worth: A Short Review

“…Deep learning or otherwise known as deep structured learning or hierarchical learning, is a section of machine learning that relies on a pack of techniques that skillfully seek to design high-level abstract models in data and perform many functions, including prediction, diagnosis, null value analysis and giving effects that support experts take the judgment [1][2][3][4][5][6][7]. Figure 1 illustrates the difference between machine learning and deep learning in object analysis.…”