Discrete Hopfield Neural Network in Restricted Maximum k-Satisfiability Logic Programming

Kasihmuddin, Mohd Shareduwan Mohd; Mansor, Mohd. Asyraf; Sathasivam, Saratha

doi:10.17576/jsm-2018-4706-30

Cited by 40 publications

(28 citation statements)

References 25 publications

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“…In another perspective, the proposed model only considers satisfiable SAT logic. Other SAT representations such as MAX k -SAT [ 18 ] require major restructuring, especially in terms logical redundancy. Furthermore, this experiment only employs a nonrestricted learning environment where the CSA and ES will iterate until

.…”

Section: Resultsmentioning

confidence: 99%

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Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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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 Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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.…”

Section: Resultsmentioning

confidence: 99%

“…Note that HNN can be split into continuous HNN (CHNN) and discrete HNN (DHNN). The structure of DHNN consists of input and output neurons that store bipolar

or binary

pattern [ 18 ]. In addition, DHNN utilizes the Lyapunov energy function to determine degree of convergence of the solution [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

Self Cite

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“…In the aspect of the 2SAT logical rule, we only utilize the satisfiable logical rule, where the Min f (w i ) − y i is always zero. The use of other non-satisfiable logic, such as maximum satisfiability [75], is only compatible for P learn = 0. The lists of parameters used in each RBFNN-2SATRA model are summarized in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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“…as the states. The spin points of the neuron flip until equilibrium is reached (Kasihmuddin et al, 2018). In terms of 3-SAT representation, each variable in 3-SAT formula will be represented in terms of N neurons.…”

Section: Hopfield Neural Networkmentioning

confidence: 99%

“…Theoretically, the Hopfield neural network is a powerful network due to the flexibility of the network to works with metaheuristics and data mining approach (Kasihmuddin et al, 2018). The capabilities of the Hopfield neural network to truncate the outliers and store the data effectively via Content Addressable Memory are essential in knowledge discovery or logic mining (Sathasivam, 2010).However, on the theoretical side, Cabrera and Sossa (2018) have enhanced the stable states of HNN to be a robust network.…”

Section: Introductionmentioning

confidence: 99%

3-Satisfiability Data Mining Technique with Hybrid Clonal Selection Algorithm for Autistic Spectrum Disorder Screening

2020

ASMSJ

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Discrete Hopfield Neural Network in Restricted Maximum k-Satisfiability Logic Programming

Abstract: Maximum k-Satisfiability ( MAX-kSAT)

Cited by 40 publications

References 25 publications

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

3-Satisfiability Data Mining Technique with Hybrid Clonal Selection Algorithm for Autistic Spectrum Disorder Screening

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