Browsing by Author "Çayır, Aykut"
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A location-based movie advisor application for android devices
Authors:Arsan, Taner; Çayır, Aykut; Umur, Hande Nur; Güney, Tacettin Dogacan; Panya, Büke
Publisher and Date:(Springer Verlag, 2015)Android is one of the world’s most popular mobile platforms. There are more than 600000 applications available today’s market place. Movie advisor applications are also available in Google Play but there is no location-based movie advisor application for Android devices in Google Play and any other marketplace. A Location-Based Service is a mobile computing application that provides information and functionality to users based on their geographical location. In this study a location-based movie ...
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Bitcoin Forecasting Using ARIMA and PROPHET
Authors:Yenidoğan, Işıl; Çayır, Aykut; Kozan, Ozan; Dağ, Tugce; Arslan, Çiğdem
Publisher and Date:(IEEE, 2018)This paper presents all studies methodology and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model the performance metrics of PROPHET and AMNIA methods are compared on the same dataset. The dataset selected 16r this study starts from May 2016 and ends in March 2018 which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing ...
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Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods
Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper we propose a method which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms such as support vector machine gradient boosting machines and random forest. All of the proposed hybrid models and the above mentioned ...
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Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods
Authors:Çayır, Aykut; Yenidoğan, Işıl; Daǧ, Hasan
Publisher and Date:(Institute of Electrical and Electronics Engineers Inc., 2018)Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper, we propose a method, which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms, such as, support vector machine, gradient boosting machines, and random forest. All of the proposed hybrid models and the above mentioned ...
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Konutların Günlük Elektrik Güç Tüketimi Tahmini İçin Uygun Model Seçimi
Zamana bağlı değişim gösteren olayların modellenmesi zorlu bir veri analizi problemidir. Bu olaylardan biri olan elektrik güç tüketiminde ise veriden mevsimsel etki ve tatil günleri gibi örüntülerin öğrenilerek bir tüketim tahmin modelinin geliştirilebilmesi için klasik makine öğrenmesi ve derin öğrenme yöntemlerinden yararlanılmaktadır. Bu çalışmada, İngiltere’nin Londra şehrindeki belirli bir bölgede 30 farklı eve ait yaklaşık 3 yıllık elektrik güç tüketimi veri kümesi kullanılarak uygun bir ...
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Random CapsNet forest model for imbalanced malware type classification task
Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracies for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the ...
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Random capsule network (CAPSNET) forest model for imbalanced malware type classification task / Dengesiz sınıf dağılımına sahip kötü amaçlı yazılım sınıflandırma görevi ̇için rassal kapsül ağı (CAPSNET) orman modeli
Kötü amaçlı yazılımın davranışı, sistem koruma yazılımının stratejilerini etkileyen kötü amaçlı yazılım türlerine bağlı olarak değişir. Yapay ve/veya derin öğrenme ile güçlendirilmiş bir ̧cok kötü amaçlı yazılım sınıflandırma modeli, kötü amaçlı yazılım türlerini tahmin etmek için üstün doğruluklar elde eder. Yapay öğrenme tabanlı modeller performanslarını büyük ölçüde etkileyen ağır öznitelik mühendisliği çalışmalarına ihtiyaç duyarlar. ̈Ote yandan, derin öğrenme tabanlı modeller, yapay öğrenme ...
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Website category classification using fine-tuned BERT language model
Authors:Demirkıran, Ferhat; Çayır, Aykut; Ünal, Uğur; Daǧ, Hasan
Publisher and Date:(Institute of Electrical and Electronics Engineers Inc., 2020)The contents on the Word Wide Web is expanding every second providing web users a rich content. However, this situation may cause web users harm rather than good due to its harmful or misleading information. The harmful contents can contain text, audio, video, or image that can be about violence, adult contents, or any other harmful information. Especially young people may readily be affected with these harmful information psychologically. To prevent youth from these harmful contents, various web ...