Major 2 Satisfiability Logic in Discrete Hopfield Neural Network

Alway, Alyaa; Zamri, Nur Ezlin; Karim, Syed Anayet; Mansor, Mohd. Asyraf; Kasihmuddin, Mohd Shareduwan Mohd; Bazuhair, Muna Mohammed

doi:10.1080/00207160.2021.1939870

Cited by 30 publications

(18 citation statements)

References 44 publications

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“…In this case, the proposed HNN-2SATABC need to consider other fitness function, such as solution diversification which increase the potential search space of HNN-2SATABC. In addition, the work can be extended by using the different optimal logical structures of Weighted Random 2 Satisfiability (r2SAT) [38], PRO2SAT [39], YRAN2SAT [40], Major 2 Satisfiability (MAJ2SAT) [41], Random 3 Satisfiability (RAN3SAT) [42], and GRAN3SAT [43].…”

Section: Discussionmentioning

confidence: 99%

Artificial Bee Colony for Logic Mining in Credit Scoring

Jamaludin

Sa’ari

Kasihmuddin

et al. 2022

Mal. J. Fund. Appl. Sci.

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During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models.

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Section: Discussionmentioning

confidence: 99%

Artificial Bee Colony for Logic Mining in Credit Scoring

Jamaludin

Sa’ari

Kasihmuddin

et al. 2022

Mal. J. Fund. Appl. Sci.

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“…Therefore, the implementation of several possible permutations for P 1 2SAT such as in Eqs. ( 5) and (6).…”

Section: Permutation In 2 Satisfiability Based Reverse Analysis Methodsmentioning

confidence: 99%

“…This shows that the interpretation of the 2SAT logical rule in DHNN can be further optimized using optimization algorithm. The implementation of 2SAT in various network inspires the emergence of other useful logic such as [5][6][7][8][9] in doing DHNN. Various type of logical rule creates optimal modelling of DHNN that has wide range of behavior.…”

Section: Introductionmentioning

confidence: 99%

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Jamaludin¹,

Mansor²,

Baharum³

et al. 2023

Computers, Materials &Amp; Continua

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The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and timeseries datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.

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“…where |z denotes a ground state in an Hilbert space of n qubits. In this paper, we construct the objective Hamiltonian of the 2-SAT problem [27] (see appendix for details appendix A), which can be expressed as an Ising model of the 2-local Hamiltonian:…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

Zhao,

Cheng,

Wang

et al. 2024

New J. Phys.

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To address the issue of the Quantum Approximate Optimization Algorithm (QAOA) frequently encountering local minima and the cost of parameter optimization within complex non-convex optimization energy landscapes, we consider a warm-start method. This approach leverages the characteristics of transition states (TS) in the enhanced optimizer, specifically descending along unique negative curvature directions, to find smaller local minima. Our research results indicate that with the assistance of an enhanced pre-training structure of the AlphaZero AI model, the initialization generalization ability of the new optimizer is significantly enhanced across various test sets. We train on 2-SAT training sets with clause densities between α≅ 2.6 and α≅ 2.89, and transfer to more complex test sets. Additionally, the average residual energy density in transfer learning consistently remains below 0.01, even achieving a high transfer success probability of 98% in hard instances with α≅ 3.7. The search efficiency, pre-trained by ensemble learning, was significantly enhanced, while only requiring simple interpolation of a few transition points to transfer on the global optimal solutions at higher sample clause densities.

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Major 2 Satisfiability Logic in Discrete Hopfield Neural Network

Cited by 30 publications

References 44 publications

Artificial Bee Colony for Logic Mining in Credit Scoring

Artificial Bee Colony for Logic Mining in Credit Scoring

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation燨perator

Artificial intelligence warm-start approach: optimizing the generalization capability of QAOA in complex energy landscapes

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