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dc.contributor.advisorAhmed, Tarem
dc.contributor.advisorAdnan, Mohammad Abdur Rahman
dc.contributor.authorLushan, Mihodi Hasan
dc.contributor.authorBhattacharjee, Manoshi
dc.date.accessioned2018-05-22T07:10:48Z
dc.date.available2018-05-22T07:10:48Z
dc.date.copyright2018
dc.date.issued2018-04
dc.identifier.otherID 14101222
dc.identifier.otherID 14301016
dc.identifier.urihttp://hdl.handle.net/10361/10196
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 24-26).
dc.description.abstractOur lives are becoming busier day by day. We are consequently forced to delegate important activities to other people. In developing countries, the middle class often have paid drivers pick up their children from schools. What if the driver decides to deviate from the usual route into a seedy part of town with the child? What if it speeds and is driving recklessly? What if it gets into an accident? In our countries like us, supervising our vehicles when we are not present in it, and being notified if anyone else using it for any unwanted/illegal intention is of paramount importance in our country. Alarms are annoying, and we want to improvise the system in a smarter way for smarter monitoring. The proposed system is developed by applying Linear Regression models, kth-Nearest-Neighbor and Support Vector Machine classifier to identify a pattern and detect abnormal behavior of the vehicle.en_US
dc.description.statementofresponsibilityMihodi Hasan Lushan
dc.description.statementofresponsibilityManoshi Bhattacharjee
dc.format.extent26 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses 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.subjectMachine learning
dc.subjectPattern recognition
dc.subjectKOAD algorithm
dc.subjectKNN algorithm
dc.subjectLinear regression
dc.subjectSupport Vector Machine (SVM)
dc.subjectGlobal Positioning System(GPS)
dc.titleSupervising vehicle using pattern recognitionen_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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