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Applying tensorflow with convolutional neural networks to train data and recognize national flags

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dc.contributor.authorDuc, H.H.-
dc.contributor.authorJung, K.-
dc.date.available2019-04-10T09:58:18Z-
dc.date.created2018-04-17-
dc.date.issued2017-
dc.identifier.isbn9789811050404-
dc.identifier.issn1876-1100-
dc.identifier.urihttp://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/32489-
dc.description.abstractIn the recent years, machine learning and deep learning has been becoming hot research titles. In many human life’s fields, AI takes big roles like auto driving a car, automatically working robots or vacuum cleaner using image recognition techniques. Tensorflow is a machine learning system with open source code was introduced and provided by Google on November 9, 2015. It has been being famously used in images recognition field. In our work, we recognize an image and classify it using tensorflow based on Convolutional Neural Networks (CNNs) and determine what it is. We train 5-layers CNNs by supervised learning from a database. After training process, trained data files are generated. In the next steps, we use this data to recognize input image and classify it. Finally, we test the results by a testing program. © Springer Nature Singapore Pte Ltd. 2017.-
dc.publisherSpringer Verlag-
dc.relation.isPartOfLecture Notes in Electrical Engineering-
dc.titleApplying tensorflow with convolutional neural networks to train data and recognize national flags-
dc.typeConference-
dc.identifier.doi10.1007/978-981-10-5041-1_60-
dc.type.rimsCONF-
dc.identifier.bibliographicCitation12th International Conference on Future Information Technology, FutureTech 2017, v.448, pp.367 - 373-
dc.description.journalClass2-
dc.identifier.scopusid2-s2.0-85019723570-
dc.citation.conferenceDate2017-05-22-
dc.citation.endPage373-
dc.citation.startPage367-
dc.citation.title12th International Conference on Future Information Technology, FutureTech 2017-
dc.citation.volume448-
dc.contributor.affiliatedAuthorJung, K.-
dc.type.docTypeConference Paper-
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