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dc.contributor.advisorKhriyenko, Oleksiy
dc.contributor.authorBanstola, Ram
dc.date.accessioned2020-12-11T07:22:03Z
dc.date.available2020-12-11T07:22:03Z
dc.date.issued2020
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/73111
dc.description.abstractDeep learning is a branch of machine learning which itself is a branch of Artificial Intelligence. The use of deep learning to solve domain specific problems is on the rise. Deep learning has been successfully used to assist sales prediction in retail, disease detection in medicine, road infrastructure monitoring by checking cracks on the road, accidents prone zones, detect anomalous activities in the realm of cyber security etc. At present, user and machine generated data is available abundantly and the challenges for enterprises is to infer new information from the available data to increase profit for the enterprise, produce a reliable system and increase customer satisfaction. Deep learning has been successfully used in classification of data with high precision. However, there are bottlenecks when it comes to anomalies in data because building models to detect anomalies is more difficult than classification problems. This thesis aims to study image anomalies detection and their applications department store using design science research methods. This thesis presents a basic prototype application to demonstrate anomalies in product areas in department stores.en
dc.format.extent80
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subject.otherdeep learning
dc.subject.otheranomaly detection
dc.subject.otherautoencoder
dc.subject.otherretail robot
dc.titleApplications of images anomalies detection using deep learning in department store
dc.identifier.urnURN:NBN:fi:jyu-202012117057
dc.type.ontasotPro gradu -tutkielmafi
dc.type.ontasotMaster’s thesisen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformaatioteknologiafi
dc.contributor.laitosInformation Technologyen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.rights.copyrightJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rights.copyrightThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.type.publicationmasterThesis
dc.contributor.oppiainekoodi602
dc.subject.ysokonenäkö
dc.subject.ysokoneoppiminen
dc.subject.ysotekoäly
dc.subject.ysoanomaliat
dc.subject.ysocomputer vision
dc.subject.ysomachine learning
dc.subject.ysoartificial intelligence
dc.subject.ysoanomalies
dc.format.contentfulltext
dc.type.okmG2


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