Vulnerability Assessment and Computationally Efficient Solution for Protecting Adversarial Deep Learning Attacks Using Parallelism

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dc.contributor.guidePawale, Sanjesh
dc.coverage.spatial
dc.creator.researcherIngle, Ganesh Balbhim
dc.date.accessioned2024-09-11T04:12:56Z
dc.date.available2024-09-11T04:12:56Z
dc.date.awarded2024
dc.date.completed2024
dc.date.registered2020
dc.description.notebibliography p. from 112 to 128
dc.format.accompanyingmaterialNone
dc.format.dimensions
dc.format.extentpage numbers 130
dc.identifier.urihttp://hdl.handle.net/10603/588587
dc.languageEnglish
dc.publisher.institutionComputer Engineering
dc.publisher.placePune
dc.publisher.universityVishwakarma University
dc.relation210
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science Information Systems, Engineering and Technology
dc.subject.keywordGenetic Algorithm, Computer Science, Machine learning
dc.subject.keywordGraph Neural Networks, Adversarial Attacks, K Means Clustering
dc.titleVulnerability Assessment and Computationally Efficient Solution for Protecting Adversarial Deep Learning Attacks Using Parallelism
dc.title.alternative
dc.type.degreePh.D.

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