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dc.contributor.advisorMostakim, Moin
dc.contributor.authorMojumder, Uttam
dc.contributor.authorSarker, Toqi Tahamid
dc.contributor.authorMonika, Gulnahar Mahbub
dc.contributor.authorRatul, Nurul Amin
dc.date.accessioned2017-01-31T04:16:00Z
dc.date.available2017-01-31T04:16:00Z
dc.date.copyright2016
dc.date.issued12/14/2016
dc.identifier.issnID 13201033
dc.identifier.otherID 11110005
dc.identifier.otherID 12201007
dc.identifier.otherID 12201089
dc.identifier.urihttp://hdl.handle.net/10361/7715
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 42-43).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.description.abstractOur world is going through a constant phase of growth and advancement where the manufacture of vehicles has increased exponentially. Vehicles of different brands with different features and models are vastly available in all over the world. Along with the fact that vehicles are now a basic need, every individual is now able to afford a vehicle of their choice and status. Consequently, misdemeanors such as theft, accidents, damage done, relating to automobiles has also increased over the years. Identifying a specific model vehicle among these several brands of vehicles can be considered difficult. Our main goal is to find the details of a specific model of a transport from several unknown automobile’s datasets. Our system will help to identify a vehicle and its model using still pictures of any brand of car. We hope that in future we can extend it to a more advanced identifying system which can be used by the government to reduce all forms of transgressions towards vehicles.en_US
dc.description.statementofresponsibilityUttam Mojumder
dc.description.statementofresponsibilityToqi Tahamid Sarker
dc.description.statementofresponsibilityGulnahar Mahbub Monika
dc.description.statementofresponsibilityNurul Amin Ratul
dc.format.extent43 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectVehicle model identificationen_US
dc.subjectNeural network approachesen_US
dc.titleVehicle model identification using neural network approachesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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