ÖZETMikrodizi verilerine dayanan veri madenciliği analizi, hastalık teşhisi ve farmakoloji alanlarında kullanılmaktadır. Analiz aşamasında yaşanan en önemli zorluk, mikrodizilerin yüksek boyutlu olması ve çok sayıda gereksiz öznitelik içermesidir. Bu nedenle çalışmada kullandığımız prostat kanseri mikrodizi veri kümesi üzerinde öznitelik boyut azaltılması amacıyla Temel bileşenler analizi (TBA) ve Parçacık sürü optimizasyonu (PSO) kullanılmıştır. Bu sayede hastalıkları etkileyen genler tespit edilmektedir. Boyutu azaltılmış veri kümeleri Destek Vektör Makinesi ve k-En Yakın Komşuluk sınıflayıcı yöntemlerine giriş olarak verilmiş ve sınıflandırma başarı sonuçları değerlendirilmiştir. Sonuç olarak PSO boyut azaltma yöntemi ile prostat kanserinde etkin genler belirlenmiş ve 50 öznitelik ile %95.77 başarı elde edilmiştir.ABSTRACT Data mining analysis based on microarray data is used in disease diagnosis and pharmacology. The major challenge in the analysis phase is the high dimension of microarrays and the large number of unnecessary features. For this reason, Principle Component Analysis (PCA) and Particle Swarm Optimization (PSO) were used to reduce the feature dimension on the prostate cancer microarray dataset used in the study. In this way, genes that affect diseases are determined. Dimension reduced data sets are given as input to Support Vector Machine and k-Nearest neighbor classification methods and classification success results are evaluated. Finally, active genes in prostate cancer were identified by PSO dimension reduction method and 95.77% success was achieved with 50 attributes.
Classification of table fruits according to size is traditionally hand made. But human factors are the cause of faulty classifications. Automatically performing this process with the machines is important in terms of speeding up the process, reducing costs, and minimizing errors. In this study, weight and diameter estimations were made on "Starking" type apples using image processing techniques. Firstly 50 photographs were taken with NIR camera and 830nm long pass filter. Afterwards, edge detection algorithms and morphological operations were performed on the images to obtain the boundaries of the images. Diameter and area information obtained from the binary image were used as attributes. These attributes were given as input to Linear Regression method and estimated. As a result, 93% of the diameters of the apples and 96.5% of the weights could be estimated.
In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.
ÖzBu çalışmada, elmalardan alınan görüntüler üzerinde evrişimsel sinir ağı yöntemlerinden olan Faster R-CNN kullanılarak elmaların çürük ve sağlam olarak sınıflandırılması amaçlanmaktadır. Önerilen modelde işlem adımları sırasıyla görüntü almaönişleme, çürük bölgelerin tespit edilmesi ve elmaların sınıflandırması şeklindedir. Görüntü alma-önişleme aşamasında, tasarlanan bir görüntü alma platformu içerisinde bulunan NIR kamera kullanılmıştır. Çalışmada 100'ü çürük ve 100'ü sağlam olan toplam 200 adet elmanın her birinin 6 farklı açısından toplam 1200 adet görüntü elde edilmiştir. Önişleme aşamasında, bu görüntülere sırasıyla uyarlamalı histogram eşitleme, kenar bulma, morfolojik işlemler uygulanmıştır. Önişlem uygulanarak görünürlüğü iyileştirilen yeni görüntüler kullanılarak eğitilen Faster R-CNN modeli ile çürük bölgeler tespit edilmiştir. Sınıflandırma aşamasında, çürük ve sağlam elmaların tespit edilmesinde %84,95 doğru sınıflandırma oranına ulaşılmıştır. Sonuç olarak, önerilen modelin meyve suyu gıda sanayisinde çürük ve sağlam elmaların otomatik olarak tespit edilmesinde kullanılabileceği düşünülmektedir. Anahtar Kelimeler"Çürük, elma, görüntü işleme, Faster R-CNN" AbstractIn this study, it is aimed to classify of the apples as bruised and robust by using Faster R-CNN which is one of the convolutional neural network methods on images taken from apple fruit. In the proposed model, the process steps are the image acquisitionpreprocessing, the determination of the caries regions, and the classification of the apples. During the image acquisitionpreprocessing phase, a NIR camera is used, which is located within a designed image acquisition platform. In the study, a total of 1200 images were obtained from 6 different angles of each of a total of 200 apples, 100 of which were bruised and 100 of which were robust. In the pre-processing phase, adaptive histogram equalization, edge detection, morphological operations are applied to these images, respectively. Caries were identified with the Faster R-CNN model trained using new images with improved visibility by applying preprocessing. In classification phase, 84.95% correct classification rate has been reached in the detection of bruised and robust apples. As a result, it is thought that the proposed model can be used for automatic detection of bruised and robust apples in juice food industry.
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