Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/68

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Citation - Scopus: 2
Comparison of Cost-Free Computational Tools for Teaching Physics
(IEEE, 2010) Er, Neslihan Fatma; Dağ, Hasan
It is widely accepted that it is quite difficult to engage today's students, from high schools to university, both in educational activities in class and "teaching" them physics due to their prejudices about the complexity of physics. The difficulty in capturing students' attention in class for a long time also plays a role in less effective teaching during learning activities. Research shows that students learn little from traditional lectures. According to constructivist learning theories, visual aids and hands-on activities play a major role in learning physics. In addition to laboratory work there are many computational tools for teaching physics, which help teachers and students in constructing a conceptual framework. With this in mind, this paper compares freeware and open source computational tools for teaching physics.
An Effective Rocommender Model for E-Commerce Platforms
(2017) Işık, Muhittin; Dağ, Hasan
Sahte kullanıcı hesapları, veri tabalarındaki seyreklik problemlerinden dolayı özellikle yeteri kadar kullanıcı tarafından puanlanmamış ürünlerde tavsiye algoritmalarını kolaylıkla etkileyebilmektedirler. Genellikle bu kullanıcı hesapları kendi ürününün puanını artırmak isteyen ürün sahipleri olabildiği gibi herhangi bir ürünü veya şirketi karalamak isteyen kötü niyetli kişiler de olabilmektedir. Bu durum birçok şirketin veri tabanı yoğunluğunun %1 den daha az olduğu düşünülürse e-ticaret ortamlarına nasıl bir etki yarattığı tahmin edilebilir. Bu çalışmada, sahte hesapların e-ticaret ortamlarında oluşturdukları negatif etkilerin üstesinden gelebilmek için, kullanıcılar arasındaki ilişkiler analiz edilerek diğer kullanıcılar üzerinde etkisi olan ve gerçekten güvenilir olduğu düşünülen kullanıcılar bulunarak bir tavsiye modeli oluşturulmaktadır. Böylece, güvenilir kullanıcıların düşüncelerinden yola çıkılarak e-ticaret ortamlarında kullanıcılara tavsiyelerde bulunan Tavsiye Sistemlerinin (TS) kalitesini artıracak bir tavsiye sistemi oluşturulacaktır
Citation - WoS: 39
Citation - Scopus: 54
Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods
(Institute of Electrical and Electronics Engineers Inc., 2018) Çayır, Aykut; Yenidoğan, Işıl; Dağ, Hasan
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 machine learning algorithms are trained on three different datasets: MNIST, Fashion-MNIST, and CIFAR-10. Results show that the proposed hybrid models are more successful than traditional models while they are being trained from raw pixel values. In this study, we empower traditional machine learning algorithms for classification using feature extraction ability of deep neural network architectures and we are inspired by transfer learning methodology to this.
Citation - WoS: 18
Citation - Scopus: 37
The Impact of Text Preprocessing on the Prediction of Review Ratings
(Tubitak, 2020) Işık, Muhittin; Dağ, Hasan
With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing strategies are found out to increase the classification accuracy. In this paper, we concentrate on the impact of simple text preprocessing decisions in order to predict fine-grained review rating stars whereas the majority of previous work focused on the binary distinction of positive vs. negative. Therefore, the aim of this research is to analyze preprocessing techniques and their influence, at the same time explain the interesting observations and results on the performance of a five-class-based review rating classifier.
Konutların Günlük Elektrik Güç Tüketimi Tahmini İçin Uygun Model Seçimi
(Fırat Üniv. Fen Bilimleri Enst., 2018) Çayır, Aykut; Dağ, Hasan
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 kısa vadeli tüketim tahmin modelinin makine öğrenmesi algoritmaları ile bulunması amaçlanmıştır.
Citation - WoS: 3
Citation - Scopus: 6
Machine Learning Model To Predict an Adult Learner's Decision To Continue Esol Course or Not
(Springer, 2019) Dahman, Mohammed R.; Dağ, Hasan
This study investigated the ability of the demographic and the affective variables to predict the adult learners' decision to continue ESOL courser. 278 adult learners, enrolled on ESOL course at FLS institution in Istanbul, Turkey, participated in the study. The result showed that the continued or dropped out groups, demonstrated statistical differences in the demographic variable (the placement test score) with a magnitude of large effect size (.378). Additionally, the result showed the effect size in the perception of the affective variables (motivation, attitude, and anxiety), accounts for about 50% of the variation between the continuation and dropout groups. Following that, three machine learning models were proposed; all possible subset regression analysis was used to compare the three models. The adequate model, which fitted the demographic variable (the placement test score) and the affective variables (motivation, attitude, and anxiety), correctly predicted 83.3% of the adult learners' decision to continue ESOL course. The model showed about 68% goodness-of-fit. The cultural implications of these findings are discussed, along with suggestions for future research.
Citation - WoS: 5
Citation - Scopus: 9
Protecting Openflow Switches Against Denial of Service Attacks
(Institute of Electrical and Electronics Engineers Inc., 2018) Bahaa-Eldin, Ayman M.; ElDessouky, Ebada Mohamed Essam Eldin Ibrahim; Dağ, Hasan
This paper presents a novel approach to protect Openflow switches against a type of Denial of Service (DoS) attacks. Openflow switches are the core of Software Defined Networks (SDN) and they are very flexible programmable and can be used for several functionalities within a network. As the control algorithm of the switch is implemented on a separate computer (Controller) this software can be implemented on any part of the network packet including Layers 2 3 and 4 headers. Therefore an Openflow switch can work as a conventional switch a router or a firewall. The open design of Openflow makes it vulnerable to several types of DoS attacks. One of those attacks is to overwhelm the switch flow table with entities larger than its buffer making legitimate packets unable to traverse the switch. The proposed approach depends on a Sandbox like model where a second switch and controller is implemented and all new packets with no matching rules are forwarded to the Sandbox. The Sandbox clone is monitored and controlled so a forwarding rule is always created on the Sandbox switch and transferred only to the working switch when it is classified as a normal rule. Otherwise a cleanup operation is executed periodically on the sandbox switch to remove malicious rules. Rules are classified based on the statistics entries already existing in Openflow switches flow table. The proposed approach is simple and does not need any extra memory or modifications in the switches. It is proven to mitigate this type of DoS attacks. © 2017 IEEE.
Citation - Scopus: 48
Random Capsnet Forest Model for Imbalanced Malware Type Classification Task
(Elsevier, 2021) Çayır, Aykut; Ünal, Uğur; Dağ, Hasan
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 machine learning-based models. However, traditional deep learning architectures components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature.
Citation - WoS: 5
Citation - Scopus: 9
Website Category Classification Using Fine-Tuned Bert Language Model
(Institute of Electrical and Electronics Engineers Inc., 2020) Demirkıran, Ferhat; Çayır, Aykut; Ünal, Uğur; Dağ, Hasan
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 filtering techniques, such as keyword filtering, Uniform Resource Locator (URL) based filtering, Intelligent analysis, and semantic analysis, are used. We propose an algorithm that can classify websites, which may contain adult contents, with 67.81% (BERT) accuracy among 32 unique categories. We also show that a BERT model gives higher accuracy than both the Sequential and Functional API models when used for text classification.

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