1. Shodhganga@INFLIBNET
  2. Manonmaniam Sundaranar University
  3. Department of Mathematics
Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/69853
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DC FieldValueLanguage
dc.coverage.spatial
dc.date.accessioned2016-01-08T11:33:20Z-
dc.date.available2016-01-08T11:33:20Z-
dc.identifier.urihttp://hdl.handle.net/10603/69853-
dc.description.abstractGraphs serve as important data structures in many applications including so newlinecial network web and Bio informatics Recursive Neural NetworksRNN are a newlineconnectionist model designed to process structured data RNN are particularly newlinesuited to process Directed Positional Acyclic GraphsDPAGs Graph Neural Net newlineworkGNN has been modeled to process data represented in graph domains con newlinesidering the topology of the graph newline newline
dc.format.extentviii, 116p.
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleNew efficient neural network approach for processing graph structured data
dc.title.alternative
dc.creator.researcherMeena rani, S M
dc.subject.keywordDirected Positional Acyclic Graphs
dc.subject.keywordGraph Neural Network
dc.subject.keywordGraph structure
dc.subject.keywordneural network
dc.subject.keywordRecursive Neural Networks
dc.description.note
dc.contributor.guideGnana jothi, R B
dc.publisher.placeTirunelveli
dc.publisher.universityManonmaniam Sundaranar University
dc.publisher.institutionDepartment of Mathematics
dc.date.registeredn.d
dc.date.completedDecember 2013
dc.date.awardedn.d
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Mathematics

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01_title.pdfAttached File40.11 kBAdobe PDFView/Open
02_certificate.pdf13.9 kBAdobe PDFView/Open
03_declaration.pdf17.81 kBAdobe PDFView/Open
04_acknowledgement.pdf14.96 kBAdobe PDFView/Open
05_content.pdf19.25 kBAdobe PDFView/Open
06_chapter 1.pdf58.96 kBAdobe PDFView/Open
07_chapter 2.pdf268.58 kBAdobe PDFView/Open
08_chapter 3.pdf760.2 kBAdobe PDFView/Open
09_chapter 4.pdf76.91 kBAdobe PDFView/Open
10_chapter 5.pdf1.53 MBAdobe PDFView/Open
11_chapter 6.pdf2.52 MBAdobe PDFView/Open
12_chapter 7.pdf21.08 kBAdobe PDFView/Open
13_reference.pdf34.35 kBAdobe PDFView/Open
14_publication.pdf14.25 kBAdobe PDFView/Open
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