A major random 1,3-satisfiability logic mining model in discrete Hopfield neural networks for adverse event analysis

Gaeithry Manoharam; Nur 'Afifah Rusdi; Nurshazneem Roslan; Nurul Atiqah Romli; Mohd Shareduwan Mohd Kasihmuddin; Nur Ezlin Zamri; Suad Abdeen; Mohd. Asyraf Mansor; Xiaoyan Liu; Gaeithry Manoharam; Nur 'Afifah Rusdi; Nurshazneem Roslan; Nurul Atiqah Romli; Mohd Shareduwan Mohd Kasihmuddin; Nur Ezlin Zamri; Suad Abdeen; Mohd. Asyraf Mansor; Xiaoyan Liu

doi:10.3934/math.2026736

AIMS Mathematics

2026, Volume 11, Issue 6: 18081-18121. doi: 10.3934/math.2026736

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A major random 1,3-satisfiability logic mining model in discrete Hopfield neural networks for adverse event analysis

Gaeithry Manoharam ¹,
Nur 'Afifah Rusdi ²,
Nurshazneem Roslan ²,
Nurul Atiqah Romli ³,
Mohd Shareduwan Mohd Kasihmuddin ^{1
,
,},
Nur Ezlin Zamri ⁴,
Suad Abdeen ¹,
Mohd. Asyraf Mansor ⁵,
Xiaoyan Liu ^1,6

1.
School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
2.
Department of Mathematical Sciences, Faculty of Intelligent Computing, Universiti Malaysia Perlis, Kampus Alam UniMAP, Pauh Putra, 02600 Arau, Perlis, Malaysia
3.
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Cawangan Perlis, Arau, 02600 Perlis, Malaysia
4.
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
5.
School of Distance Education, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
6.
School of Integrated Circuits and New Energy, Guangzhou College of Technology and Business, 510850 Guangzhou, China

Received: 06 February 2026 Revised: 16 March 2026 Accepted: 20 March 2026 Published: 18 June 2026
MSC : 68N17, 68R07, 68T27

Logic mining in neural networks enables the extracting of interpretable symbolic knowledge from complex datasets. This study proposes a logic mining approach in discrete Hopfield neural networks based on a non-systematic major random 1,3-satisfiability (MR1,3SAT) logical structure for adverse event analysis in Alzheimer's disease datasets. The proposed model incorporates first- and third-order logical clauses to enhance representational flexibility while maintaining computational tractability. A feature selection mechanism based on Jaccard analysis is incorporated to identify relevant attributes, while the intelligent artificial bee colony algorithm is applied during the retrieval phase to refine the final neuron states. Experimental evaluation on Alzheimer's disease neuroimaging initiative datasets demonstrate that the proposed model achieves superior performance compared with existing logic mining approaches, obtaining 90.23% accuracy, 98% sensitivity, 99% specificity, and a Fowlkes-Mallows index of 93.46%. These results indicate that the MR1,3SAT-based logic mining model improves both interpretability and classification reliability for adverse event symptoms analysis in Alzheimer's disease neuroimaging initiative datasets.
- Alzheimer disease,
- discrete Hopfield neural network,
- logic mining,
- depression detection,
- adverse event and Fowlkes-Mallows
Citation: Gaeithry Manoharam, Nur 'Afifah Rusdi, Nurshazneem Roslan, Nurul Atiqah Romli, Mohd Shareduwan Mohd Kasihmuddin, Nur Ezlin Zamri, Suad Abdeen, Mohd. Asyraf Mansor, Xiaoyan Liu. A major random 1,3-satisfiability logic mining model in discrete Hopfield neural networks for adverse event analysis[J]. AIMS Mathematics, 2026, 11(6): 18081-18121. doi: 10.3934/math.2026736

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Abstract

Logic mining in neural networks enables the extracting of interpretable symbolic knowledge from complex datasets. This study proposes a logic mining approach in discrete Hopfield neural networks based on a non-systematic major random 1,3-satisfiability (MR1,3SAT) logical structure for adverse event analysis in Alzheimer's disease datasets. The proposed model incorporates first- and third-order logical clauses to enhance representational flexibility while maintaining computational tractability. A feature selection mechanism based on Jaccard analysis is incorporated to identify relevant attributes, while the intelligent artificial bee colony algorithm is applied during the retrieval phase to refine the final neuron states. Experimental evaluation on Alzheimer's disease neuroimaging initiative datasets demonstrate that the proposed model achieves superior performance compared with existing logic mining approaches, obtaining 90.23% accuracy, 98% sensitivity, 99% specificity, and a Fowlkes-Mallows index of 93.46%. These results indicate that the MR1,3SAT-based logic mining model improves both interpretability and classification reliability for adverse event symptoms analysis in Alzheimer's disease neuroimaging initiative datasets.

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