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Optimizing Discrete Hopfield Neural Networks Using Major Random 1,3-Satisfiability And Hybrid Artificial Bee Colony

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dc.contributor.authorManoharam, Gaeithry
dc.date.accessioned2026-06-26T01:33:43Z
dc.date.available2026-06-26T01:33:43Z
dc.date.issued2025-09
dc.description.abstractSatisfiability is a key symbolic language in Discrete Hopfield Neural Networks that contributes to the development of advanced Artificial Intelligence models. However, the absence of combined higher-orders satisfiability limits the full potential within the network. Therefore, this thesis introduces a optimizing discrete Hopfield neural networks using major random 1,3-satisfiability and hybrid artificial bee colony to evaluate the neuron behaviours within the network. The proposed logic demonstrates optimal performance compared to existing logical rules.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24503
dc.language.isoen
dc.subjectOptimizing Discrete Hopfield Neural Networks
dc.subjectMajor Random 1
dc.subject3-Satisfiability And Hybrid Artificial Bee Colony
dc.titleOptimizing Discrete Hopfield Neural Networks Using Major Random 1,3-Satisfiability And Hybrid Artificial Bee Colony
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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