Publication: Conditional Random 2 Satisfiability In Discrete Hopfield Neural Networks For Enhanced Logic Mining
| dc.contributor.author | Roslan, Nurshazneem | |
| dc.date.accessioned | 2026-06-24T00:52:46Z | |
| dc.date.available | 2026-06-24T00:52:46Z | |
| dc.date.issued | 2025-06 | |
| dc.description.abstract | A deeper understanding of additional mechanisms in discrete hopfield neural network is essential for developing intelligent model for broader applications. This thesis introduces conditional random 2 satisfiability, a new logical rule guaranteeing the inclusion of at least one negative logic operator for each second-order clause which implements the non-monotonic smish activation function to enhance the updating process within the network. Furthermore, multi-objective function optimization using a hybrid binary whale optimization algorithm is employed during the retrieval phase to generate diversified neuron states with maximum global solutions and minimized similarity indices. Finally, a new approach for identifying the best logic based on various performance metrics in the logic mining model called conditional random 2 satisfiability reverse analysis is proposed. This approach is significant when dealing with imbalanced datasets leading to an enhanced search space for finding optimal induced logic. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/24451 | |
| dc.language.iso | en | |
| dc.subject | Conditional Random Satisfiability Discrete Hopfield Neural | |
| dc.title | Conditional Random 2 Satisfiability In Discrete Hopfield Neural Networks For Enhanced Logic Mining | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |