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Abstract. The fabric defect detection has crucial importance in terms of sectoral quality. As fabric defection stage, accordingly the growing market volume and production capacity, detection via human vision has caused largely time-wasting and success rate decreasing until 60%. Due to a fabric has unique texture, there is necessity for it to work on separately from other images types while extracting its features. Features are vital material of computer vision especially classification problems. Hence, extracting right features is the most significant stage of error detection. This purpose in mind on this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement on image and speech procession recent years by self-feature extraction is applied to fabric defect detection. Stacked autoencoder -a deep learning method-that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gained acceptable success. The principal aim of this study is to increase achievement of feature extraction by tuning up the input value and hyper parameters autoencoder. Thanks to the fine tuning of hyper-parameters of deep model, we have 96% success rate on our own dataset. Keywords: Deep learning, fabric defect detection, autoencoder, hyper parameter Kumaş Hatası Tespiti için Yığınlanmış Oto-kodlayıcı YöntemiÖzet. Kumaş hatası tespiti sektörel kalite açısından önem arz etmektedir. Bu hataların tespitinde, gelişen pazar hacmi ve üretim kapasitelerinin büyüklüğü sebebiyle insan görüsü ile tespit, büyük oranda zaman kaybına ve hata tespit oranının %60 seviyelerine kadar düşmesine sebep olmaktadır. Bu bağlamda daha yüksek başarım elde edebilmek için görüntü işleme alanında bir çok yöntem denenmiştir. Kumaşın kendine has bir dokusunun olması sebebiyle, öznitelikleri çıkarılırken diğer görüntü türlerinden ayrı olarak incelenmesi gerektirmektedir. Öznitelikler bilgisayarlı görmede özellikle sınıflandırma problemlerinde ham madde olmaktadır. Bu yüzden doğru öznitelikleri çıkarmak, hata tespitinde en önemli aşamadır. Bu amaç doğrultusunda, çoklu-katman mimarisi ve kendi özniteliklerini çıkararak son yıllarda görüntü ve ses işleme alanında büyük başarılar getirmesi ile öne çıkan derin öğrenme kumaş hatası tespitine uygulanmıştır. Giriş verisini sıkıştırma ya da genişletme ile temsil eden yığınlı oto-kodlayıcılar -bir derin öğrenme yöntemi-kumaş hatası tespitinde denenmiş ve kabul edilebilir başarılar elde edilmiştir. Çalışmanın asıl amacı oto kodlayıcının hiper parametreleri ve giriş değeri ile oynamalar yaparak öznitelik çıkarımı başarısını artırmaktır. Derin modelin hiper parametrelerin ince ayarıyla, kendi veri setimizde %96'lık bir başarı oranı elde ettik.Anahtar Kelimeler: Derin öğrenme, kumaş hatası tespiti, oto-kodlayıcı, hiper parametre

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Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from developer activities that can then be used as developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics extracted from the issue, commit, and pull request activities of developers on GitHub. We created developer metrics from the individual activities and combined certain activities according to some common traits. To evaluate these metrics, we created an item-based project recommendation system. In order to validate this system, we calculated the similarity score using two methods and assessed top-n hit scores using two different approaches. The results for all scores with these methods indicated that the most successful metrics were binary_issue_related, issue_commented, binary_pr_related, and issue_opened. To verify our results, we compared our metrics with another metric generated from a very similar study and found that most of our metrics gave better scores that metric. In conclusion, the issue feature is more crucial for GitHub compared with other features. Moreover, commenting activity in projects can be equally as valuable as code contributions. The most of binary metrics that were generated, regardless of the number of activities, also showed remarkable results. In this context, we presented improvable and noteworthy developer metrics that can be used for a wide range of open-source software development challenges, such as user characterization, project recommendation, and code review assignment.

In open-source software development environments; textual, numerical, and relationshipbased data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software engineering and natural language processing. However, since these data sets contain all the data in the environment, the problem arises in the terabytes of data processing. For this reason, almost all of the studies using GitHub data use filtered data according to certain criteria. In this context, using a different data set in each study makes a comparison of the accuracy of the studies quite difficult. In order to solve this problem, a common dataset was created and shared with the researchers, which would allow to work on many software engineering problems.

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is a hybrid artificial neural network (intelligence) approach that utilizes the ability of artificial neural networks to learn, generalize, paralyze and to derive fuzzy logic. The development of models with large numbers of input variables with ANFIS is not very convenient for applications. Dimension reduction methods are proposed as a solution to this problem. Dimensional Reduction is the method used to represent the data in a lower dimensional space. The reduction of the numbers of the input variables using different size reduction methods and the creation of the optimal solution of the probing with the ANFIS model constitute the framework of this work. In this study, we compared the results produced by different dimension reduction methods and investigated which method is more acceptable for ANFIS training.