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A Novel Dynamic Evolutionary Model Integrating Discrete Hopfield Neural Networks With Satisfiability Problems And Its Applications In Image Encryption And Decryption

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dc.contributor.authorFeng, Caicai
dc.date.accessioned2026-03-18T06:54:23Z
dc.date.available2026-03-18T06:54:23Z
dc.date.issued2025-06
dc.description.abstractThis thesis proposes a series of innovative DHNN-SAT variants and their applications. To address the inefficiency of traditional DHNN-SAT networks in solving SAT problems with dynamic constraints, a Dynamically Evolving Discrete Hopfield-SAT Neural Network with a flexible and scalable architecture is specifically designed. To tackle challenges posed by varying network scales and logical complexities, an optimized network based on a Crow Search Algorithm-guided Fuzzy Clustering Hybrid Method is proposed.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/23805
dc.language.isoen
dc.subjectEncryption
dc.subjectAlgorithms
dc.titleA Novel Dynamic Evolutionary Model Integrating Discrete Hopfield Neural Networks With Satisfiability Problems And Its Applications In Image Encryption And Decryption
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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