Open Access


Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation Operator

Siti Zulaikha Mohd Jamaludin1, Mohd. Asyraf Mansor2, Aslina Baharum3,*, Mohd Shareduwan Mohd Kasihmuddin1, Habibah A. Wahab4, Muhammad Fadhil Marsani1
1 School of Mathematical Sciences, Universiti Sains Malaysia, Minden, Penang, 11800, Malaysia
2 School of Distance Education, Universiti Sains Malaysia, Minden, Penang, 11800, Malaysia
3 Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88450, Sabah, Malaysia
4 School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, 11800, Malaysia
* Corresponding Author: Aslina Baharum. Email:

Computers, Materials & Continua 2023, 74(2), 2853-2870.

Received 25 May 2022; Accepted 12 July 2022; Issue published 31 October 2022


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 time-series 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.


Logic mining; logical permutation; discrete hopfield neural network; knowledge extraction

Cite This Article

S. Z. M. Jamaludin, M. A. Mansor, A. Baharum, M. S. M. Kasihmuddin, H. A. Wahab et al., "Modified 2 satisfiability reverse analysis method via logical permutation operator," Computers, Materials & Continua, vol. 74, no.2, pp. 2853–2870, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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