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Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we propose were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The results showed that the CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models

Bu çalışmada, Bölgesel Tabanlı Evrişimli Sinir Ağları (R-CNN) ile araç plaka lokasyonu belirleme ve belirlenen lokasyon içerisinden plaka okuma işlemi gerçekleştirilmiştir. İki aşamadan oluşan çalışmanın ilk aşamasında giriş görüntüleri üzerinden plaka lokasyonları R-CNN ile belirlenirken ikinci aşamada geleneksel görüntü işleme teknikleri ile belirlenen lokasyonlardan plaka okuma işlemi gerçekleştirilmektedir. Çalışmada tasarlanan R-CNN eğitiminde veri setinde bulunan 550 adet görüntüden 450 adedi eğitimde ve 100 adedi test işleminde kullanılmıştır. R-CNN ile plaka lokasyonu bulma işleminde test seti üzerinde %95 başarı oranına ulaşılırken doğru olarak belirlenen lokasyonlardan plaka okuma işleminde %97 başarı oranına ulaşılmıştır.

Operating systems and computer architecture and organization course are fundamental topics underlying many disciplines, including computer, electrical and electronics engineering departments. These courses involve both a theoretical and practical part for the effective learning process. A FPGA-based micro computer architecture design named BZK.SAU.FPGA10.1 was proposed to reinforce computer architecture and organization fundamentals in 2011. Also we developed an educational operating system named BZK.SAUOS from scratch on BZK.SAU.FPGA10.1 architecture. The program written using the user interface of this operating system was saved to main memory of this microcomputer architecture since it does not have a nonvolatile memory unit. So the process of editing on a file previously written is not impossible. This paper addresses an effective learning approach that permits one to work on real computer architecture and original operating system while supporting easily to save and edit their program using flash memory instead of main memory. The flash memory controller in this work have been designed completely hardware. So this controller allows the students to examine the internal structure of this controller while improving the motivation in the control of storage units like flash memory, hard disk etc. Overall, this work helps the students to improve controller design experience of storage units in low-level in addition to an increase in their learning process in the computer architecture and operating systems topics. Index Terms-BZK.SAU.FPGA, educational tool, memory controller, microcomputer architecture design, operating system. I. INTRODUCTION Operating systems and Computer architecture and organization courses are some of the main courses in the computer engineering and computer science. The effective learning process in these courses involves both a theoretical and practical part. There are several of designs and simulators in the open literature to improve the motivation in these courses. We also developed two FPGA-based microcomputer architecture designs named BZK.SAU.FPGA10.0 and BZK.SAU.FPGA10.1 in addition to literature. Then we designed an educational operating system named Manuscript

Android malware detection is a critical and important problem that must be solved for a widely used operating system. Conventional machine learning techniques first extract some features from applications, then create classifiers to distinguish between malicious and benign applications. Most of the studies available today ignore the weighting of the obtained features. To overcome this problem, this study proposes a new software detection method based on weighting the data in feature vectors to be used in classification. To this end, firstly, the manifest file was read from the Android application package. Different features such as activities, services, permissions were extracted from the file, and for classification, a selection was made among these features. The parameters obtained as a result of selection were optimized by the deep neural network model. Studies revealed that through feature selection and weighting, better performance values could be achieved and more competitive results could be obtained in weight-sensitive classification.