Students’ performance via satisfiability reverse analysis method with Hopfield Neural Network

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

doi:10.1063/1.5136467

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Discrete Hopfield Neural Network (DHNN) is depicted as a suitable ANN platform of linearized interconnected neuron states to analyze discrete entries. It is worth mentioning that DHNN has a variety of applications such as resources management [7], location detector [8], and students' performance evaluation [9]. Despite the recent and fast improvement in DHNN, there has been no recent development of demonstrating the output of DHNN in the form of the symbolic rules.…”

Section: Introductionmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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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 nonsystematic satisfiability logic is important to improve the flexibility of the logical structure. This paper proposes Random 3 Satisfiability (RAN3SAT) with three types of logical combinations (k =1, 3, k =2, 3, and k =1, 2, 3) to report the behaviors of multiple logical structures. The different types of RAN3SAT enforced with Discrete Hopfield Neural Network (DHNN) are included with benchmark searching techniques, such as Exhaustive Search algorithm. Additionally, to strengthen and certify the behavior of the proposed model, we extensively conducted several performance evaluation metrics with a specific number of neurons. In particular, the experimental results revealed that RAN3SAT was able to be implemented in DHNN, and each logical combination has its characteristics. Nonetheless, RAN3SAT provides more neuron variations in the whole solution space. The proposed model can also be applied in real-world applications such as the logic mining approach since RAN3SAT consists of various logic combinations that behave as input language to transform raw data into informative output.

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Section: Introductionmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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“…The extracted logic will represent information aligned with the specifics classification tasks. An interesting application of k -SATRA is reported by the work of [ 34 ], which investigates students’ performance in identifying related factors of underachievement students. The work entrenched several real-life data sets and obtained higher accuracy than two other existing educational data mining methods.…”

Section: Introductionmentioning

confidence: 99%

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

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Mansor

Kasihmuddin

et al. 2020

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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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Supervised Learning Perspective in Logic Mining

et al. 2022

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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 artificial neural network, tend to converge to suboptimal solution. This paper proposed a novel logic mining that integrates supervised learning via association analysis to identify the most optimal arrangement with respect to the given logical rule. By utilizing Hopfield neural network as an associative memory to store information of the logical rule, the optimal logical rule from the correlation analysis will be learned and the corresponding optimal induced logical rule can be obtained. In other words, the optimal logical rule increases the chances for the logic mining to locate the optimal induced logic that generalize the datasets. The proposed work is extensively tested on a variety of benchmark datasets with various performance metrics. Based on the experimental results, the proposed supervised logic mining demonstrated superiority and the least competitiveness compared to the existing method.

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Students’ performance via satisfiability reverse analysis method with Hopfield Neural Network

Cited by 4 publications

References 21 publications

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

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

Supervised Learning Perspective in Logic Mining

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