| dc.creator |
PENÇE, İhsan; BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, BUCAK ZELİHA TOLUNAY UYGULAMALI TEKNOLOJİ VE İŞLETMECİLİK YÜKSEKOKULU, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ |
|
| dc.creator |
ŞİŞECİ ÇEŞMELİ, Melike; BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, BUCAK ZELİHA TOLUNAY UYGULAMALI TEKNOLOJİ VE İŞLETMECİLİK YÜKSEKOKULU, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ |
|
| dc.date |
2019-01-30T00:00:00Z |
|
| dc.date.accessioned |
2019-04-09T07:22:20Z |
|
| dc.date.available |
2019-04-09T07:22:20Z |
|
| dc.identifier |
http://dergipark.gov.tr/sjmakeu/issue/42296/507685 |
|
| dc.identifier |
|
|
| dc.identifier.uri |
http://hdl.handle.net/11672/2487 |
|
| dc.description |
The process of classification of marble slabs has animportant place in terms of construction sector and demands. Despite theadvanced mines and construction equipment in Turkey and the world, theseparation of cut marble process is a problem that has not been solved yet. Thelack of a standard for the classification of marbles and the use of humanfactors for this process lead to erroneous and inefficient determinations. Inthis study, for the first time the Deep Learning method has been tried onmarbles, and the components obtained from Deep Learning layers have beenexamined and the success of classification has measured. Thanks to thesuccessful results, the basics of the Deep Learning network have been laid forfuture marble databases. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Mehmet Akif Ersoy University |
|
| dc.publisher |
Mehmet Akif Ersoy Üniversitesi |
|
| dc.relation |
http://dergipark.gov.tr/download/article-file/637062 |
|
| dc.source |
Volume: 2, Issue: 1
21-26 |
|
| dc.source |
2651-3722 |
|
| dc.subject |
Deep Learning,Marble,Image Processing, Convolutional Neural Networks |
|
| dc.title |
Deep Learning in Marble Slabs Classification |
|
| dc.type |
info:eu-repo/semantics/article |
|