Yıl 2019, Cilt 21 , Sayı 2, Sayfalar 386 - 396 2019-08-15

Data Mining and Pixel Distribution Approach for Wood Density Prediction
Odun Yoğunluğu Tahmini için Veri Madenciliği ve Piksel Dağılımı Yaklaşımı

Timuçin BARDAK [1] , Selahattin BARDAK [2] , Eser SÖZEN [3]


Wood has a strategic importance in economic development. Innovations are the basic premise of commercial success in the wood industry, as in all industries. The density of wood provides valuable information about the physical and mechanical properties of the wood, and it is also directly related to the productivity in the forest industry. Many non-destructive test studies have been conducted to evaluate the physical properties of wood structures. This study was conducted to predict the density of wood in oak (Quercus robur) and beech (Fagus orientalis L.) using the number of pixels in grayscale image and data mining. To this purpose, pixel density of data were saved from wood images. This data was used as descriptor variables in artificial neural networks and random forest algorithm. The designed artificial neural network model and random forest algorithm allowed the prediction of density with an accuracy of 95.19% and 96.36%, respectively for the testing phase. As a result, this study showed that pixel density and data mining have the potential to be used as an instrument for predicting the density of wood

Ahşap ekonomik kalkınmada stratejik bir öneme sahiptir. Yenilikler, tüm endüstrilerde olduğu gibi ahşap endüstrisinde de ticari başarının temelini oluşturur. Ahşabın yoğunluğu, ahşabın fiziksel ve mekanik özellikleri hakkında değerli bilgiler sağlar ve ayrıca orman endüstrisindeki verim ile de doğrudan ilgilidir. Ahşap yapıların fiziksel özelliklerini değerlendirmek için birçok tahribatsız test çalışmaları yapılmıştır. Bu çalışma, gri tonlamalı görüntüdeki piksel sayısı ve veri madenciliğini kullanarak meşe (Quercus robur) ve kayın (Fagus orientalis L.) ağacının yoğunluğunu tahmin etmek için yapıldı. Bu amaçla, ahşap görüntülerden elde edilen piksel yoğunluğu verileri kaydedildi. Bu veriler yapay sinir ağları ve rastgele orman algoritmalarında tanımlayıcı değişkenler olarak kullanılmıştır. Tasarlanan yapay sinir ağı ve rastgele orman algoritmaları, test aşamasında sırasıyla % 95,19 ve          % 96,36 doğrulukla yoğunluk tahmini sağladı. Sonuç olarak, bu çalışma piksel yoğunluğunun ve veri madenciliğinin ahşabın yoğunluğunu öngörmede bir araç olarak kullanılma potansiyeline sahip olduğunu göstermiştir.

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Birincil Dil en
Konular Malzeme Bilimleri, Kâğıt ve Ahşap
Bölüm Wood Machinary, Occupational Safety and Health, Business Administration
Yazarlar

Orcid: 0000-0002-1403-1049
Yazar: Timuçin BARDAK
Kurum: BARTIN ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0001-9724-4762
Yazar: Selahattin BARDAK (Sorumlu Yazar)
Kurum: SİNOP ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0003-4798-7124
Yazar: Eser SÖZEN
Kurum: BARTIN ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 15 Ağustos 2019

Bibtex @araştırma makalesi { barofd561858, journal = {Bartın Orman Fakültesi Dergisi}, issn = {1302-0943}, eissn = {1308-5875}, address = {}, publisher = {Bartın Üniversitesi}, year = {2019}, volume = {21}, pages = {386 - 396}, doi = {}, title = {Data Mining and Pixel Distribution Approach for Wood Density Prediction}, key = {cite}, author = {BARDAK, Timuçin and BARDAK, Selahattin and SÖZEN, Eser} }
APA BARDAK, T , BARDAK, S , SÖZEN, E . (2019). Data Mining and Pixel Distribution Approach for Wood Density Prediction. Bartın Orman Fakültesi Dergisi , 21 (2) , 386-396 . Retrieved from https://dergipark.org.tr/tr/pub/barofd/issue/43738/561858
MLA BARDAK, T , BARDAK, S , SÖZEN, E . "Data Mining and Pixel Distribution Approach for Wood Density Prediction". Bartın Orman Fakültesi Dergisi 21 (2019 ): 386-396 <https://dergipark.org.tr/tr/pub/barofd/issue/43738/561858>
Chicago BARDAK, T , BARDAK, S , SÖZEN, E . "Data Mining and Pixel Distribution Approach for Wood Density Prediction". Bartın Orman Fakültesi Dergisi 21 (2019 ): 386-396
RIS TY - JOUR T1 - Data Mining and Pixel Distribution Approach for Wood Density Prediction AU - Timuçin BARDAK , Selahattin BARDAK , Eser SÖZEN Y1 - 2019 PY - 2019 N1 - DO - T2 - Bartın Orman Fakültesi Dergisi JF - Journal JO - JOR SP - 386 EP - 396 VL - 21 IS - 2 SN - 1302-0943-1308-5875 M3 - UR - Y2 - 2019 ER -
EndNote %0 Bartın Orman Fakültesi Dergisi Data Mining and Pixel Distribution Approach for Wood Density Prediction %A Timuçin BARDAK , Selahattin BARDAK , Eser SÖZEN %T Data Mining and Pixel Distribution Approach for Wood Density Prediction %D 2019 %J Bartın Orman Fakültesi Dergisi %P 1302-0943-1308-5875 %V 21 %N 2 %R %U
ISNAD BARDAK, Timuçin , BARDAK, Selahattin , SÖZEN, Eser . "Data Mining and Pixel Distribution Approach for Wood Density Prediction". Bartın Orman Fakültesi Dergisi 21 / 2 (Ağustos 2019): 386-396 .
AMA BARDAK T , BARDAK S , SÖZEN E . Data Mining and Pixel Distribution Approach for Wood Density Prediction. Bartın Orman Fakültesi Dergisi. 2019; 21(2): 386-396.
Vancouver BARDAK T , BARDAK S , SÖZEN E . Data Mining and Pixel Distribution Approach for Wood Density Prediction. Bartın Orman Fakültesi Dergisi. 2019; 21(2): 396-386.