Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/504553
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dc.coverage.spatialComputer Science
dc.date.accessioned2023-08-03T11:43:33Z-
dc.date.available2023-08-03T11:43:33Z-
dc.identifier.urihttp://hdl.handle.net/10603/504553-
dc.description.abstractfile attached
dc.format.extent91 p.
dc.languageEnglish
dc.relationno. of references 47
dc.rightsuniversity
dc.titleDetection and classification of suspicious objects in surveillance video using machine learning
dc.title.alternative-
dc.creator.researcherMangrolia , Jayandrath R
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Interdisciplinary Applications
dc.subject.keywordEngineering and Technology
dc.subject.keywordGradient Direction and Laplacian Magnitude based Histogram
dc.subject.keywordMixture of Gaussian
dc.subject.keywordNeural Network
dc.subject.keywordPrincipal Component Analysis
dc.subject.keywordSupport Vector Machine
dc.description.notebibliography p.85-89
dc.contributor.guidePatel, Chintan
dc.publisher.placeAhmedabad
dc.publisher.universityRaksha Shakti University
dc.publisher.institutionSchool of Information Technology, Artificial Intelligence and Cyber Security (SITAICS)
dc.date.registered
dc.date.completed2022
dc.date.awarded2023
dc.format.dimensions-
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Information Technology

Files in This Item:
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01_title.pdfAttached File201.77 kBAdobe PDFView/Open
02_prelim pages.pdf1.38 MBAdobe PDFView/Open
03_content.pdf205.9 kBAdobe PDFView/Open
04_abstract.pdf202.25 kBAdobe PDFView/Open
05_list of abbreviations.pdf143.46 kBAdobe PDFView/Open
06_chapter 1.pdf218.57 kBAdobe PDFView/Open
07_chapter 2.pdf364 kBAdobe PDFView/Open
08_chapter 3.pdf257.96 kBAdobe PDFView/Open
09_chapter 4.pdf155.62 kBAdobe PDFView/Open
10_chapter 5.pdf373.32 kBAdobe PDFView/Open
11_chapter 6.pdf629 kBAdobe PDFView/Open
12_chapter 7.pdf225.02 kBAdobe PDFView/Open
13_bibliography.pdf184.04 kBAdobe PDFView/Open
80_recommendation.pdf156.1 kBAdobe PDFView/Open


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