Browsing by Author "Peker, Serhat"

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Ağ anomalilerinin tespitinde kullanılan yapay sinir ağlarının karşılaştırılması
(2019) Öney, Mehmet Uğur; Peker, Serhat; Software Engineering
Ağ saldırı tespit sistemleri günümüz bilişim sistemlerinde kritik bir yer teşkil ederken önemli bir araştırma alanı olarak yükselmeye ve yapay sinir ağlarının kullanımı bu alanda giderek daha popüler hale gelmeye başlamıştır. Buna rağmen, bu alanda yapay sinir ağı mimarileri ve bu mimarilerin bileşen parametreleri hakkında kapsamlı bir karşılaştırmalı çalışmasının eksikliği vardır. Bu çalışmada, ağ saldırı tespit sistemleri alanında kullanılan yapay sinir ağları mimarileri ve bu mimarilerin bileşenleri olan optimizasyon fonksiyonları, aktivasyon fonksiyonları, öğrenme kat sayısı ve momentum değişiminin doğruluk ve hatalı uyarı üretme oranlarına göre kıyaslayarak ileride yapılacak olan mühendislik ve akademik çalışmalar için bir temel oluşturması amaçlanmıştır. Bu doğrultuda, 6480 adet yapay sinir ağı oluşturularak kıyaslama veri kümesi olarak kabul edilen KDD99 ve yakın gerçek zamanlı simülasyon ortamı yardımıyla her bir yapay sinir ağı değerlendirilmiştir. Bu tezin, yapay sinir ağları kullanılarak geliştirilecek ağ saldırı tespit sistemleri araştırmalarına rehberlik edecek bir yol haritası sağlayacaktır.
Ahp kullanılarak mobil uygulamaların geliştirilmesi için kullanım durumlarını önceliklendirme: yapılacaklar listesi uygulamalarında bir vaka çalışması
(2019) Yıldırım, Onur; Peker, Serhat; Yazıcı, Ali; Software Engineering
Mobil uygulamaların kullanım yoğunluğu, iletişim teknolojilerinin hızlı gelişimi ile bağlantılı olarak önemli ölçüde artmıştır. Her gün pek çok farklı Yapılacaklar listesi uygulaması mobil uygulama pazarlarına yüklenmektedir. Ancak, uygulamaların rekabetçi kalması ve bu pazarlarda hayatta kalması çok zordur. Mobil uygulama pazarındaki başarı faktörlerinden biri mobil uygulamaların işlevselliğidir. Uygulamanın işlevlerini doğru tanımlamak, mobil uygulama geliştiricileri tarafından pazar gücünü etkiler. Böylece, bu çalışma mobil uygulamaların işlevlerine öncelik vermeyi amaçlamaktadır. Bu amaçla, mobil uygulamaların geliştirilmesine yönelik kullanım durumlarını değerlendirmek için Analitik Hiyerarşi Süreci (AHP) kullanılır. Yapılacaklar listesi uygulamalarında uygulanan durum incelemesinin sonuçları, AHP'nin mobil uygulamalardaki gereksinimlerin önemini belirlemek için etkili bir araç olarak kullanılabileceğini göstermektedir.
Citation Count: 9
A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining
(Ieee-inst Electrical Electronics Engineers inc, 2020) Guney, Sinem; Peker, Serhat; Turhan, Cigdem; Software Engineering
The purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: "high consuming-valuable subscribers", "less consuming subscribers","less consuming-loyal subscribers" and "disloyal subscribers". In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.
Citation Count: 0
A Comparison of Neural Network Approaches for Network Intrusion Detection
(Springer international Publishing Ag, 2020) Oney, Mehmet Ugur; Peker, Serhat; Software Engineering
Nowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.
Citation Count: 4
The Effects of the Content Elements of Online Banner Ads on Visual Attention: Evidence from An-Eye-Tracking Study
(Mdpi, 2021) Peker, Serhat; Menekse Dalveren, Gonca Gokce; Inal, Yavuz; Software Engineering; Information Systems Engineering
The aim of this paper is to examine the influence of the content elements of online banner ads on customers' visual attention, and to evaluate the impacts of gender, discount rate and brand familiarity on this issue. An eye-tracking study with 34 participants (18 male and 16 female) was conducted, in which the participants were presented with eight types of online banner ads comprising three content elements-namely brand, discount rate and image-while their eye movements were recorded. The results showed that the image was the most attractive area among the three main content elements. Furthermore, the middle areas of the banners were noticed first, and areas located on the left side were mostly noticed earlier than those on the right side. The results also indicated that the discount areas of banners with higher discount rates were more attractive and eye-catching compared to those of banners with lower discount rates. In addition to these, the participants who were familiar with the brand mostly concentrated on the discount area, while those who were unfamiliar with the brand mostly paid attention to the image area. The findings from this study will assist marketers in creating more effective and efficient online banner ads that appeal to customers, ultimately fostering positive attitudes towards the advertisement.
Citation Count: 6
An empirical comparison of customer behavior modeling approaches for shopping list prediction
(Institute of Electrical and Electronics Engineers Inc., 2018) Peker,S.; Kocyigit,A.; Erhan Eren,P.; Software Engineering
Shopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction. © 2018 Croatian Society MIPRO.
Citation Count: 0
An Empirical Comparison of Customer Behavior Modeling Approaches for Shopping List Prediction
(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. Erhan; Software Engineering
Shopping list prediction is a crucial task for companies as it can enable to provide a specific customer a personalized list of products and improve customer satisfaction and loyalty as well. To predict customer behaviors, many studies in the literature have employed customer behavior modeling approaches which are individual-level and segment-based. However, previous efforts to predict customers' shopping lists have rarely employed these state-of-the-art approaches. In this manner, this paper introduces the segment based approach into the shopping list prediction and then presents an empirical comparison of the individual-level and the segment-based approaches in this problem. For this purpose, well-known machine learning classifiers and customers' purchase history are employed, and the comparison is performed on a real-life dataset by conducting a series of experiments. The results suggest that there is no clear winner in this comparison and the performances of customer behavior modeling approaches depend on the machine learning algorithm employed. The study can help researchers and practitioners to understand different aspects of using customer behavior modeling approaches in the shopping list prediction.
Citation Count: 12
A hybrid approach for predicting customers' individual purchase behavior
(Emerald Group Publishing Ltd, 2017) Peker, Serhat; Kocyigit, Altan; Eren, P. Erhan; Software Engineering
Purpose - Predicting customers' purchase behaviors is a challenging task. The literature has introduced the individual-level and the segment-based predictive modeling approaches for this purpose. Each method has its own advantages and drawbacks, and performs in certain cases. The purpose of this paper is to propose a hybrid approach which predicts customers' individual purchase behaviors and reduces the limitations of these two methods by combining the advantages of them. Design/methodology/approach - The proposed hybrid approach is established based on individual-level and segment-based approaches and utilizes the historical transactional data and predictive algorithms to generate predictions. The effectiveness of the proposed approach is experimentally evaluated in the domain of supermarket shopping by using real-world data and using five popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings - A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based approaches in terms of prediction coverage while maintaining roughly comparable prediction accuracy to the individual-level method. Moreover, the experimental results demonstrate that logistic regression performs better than the other classifiers in predicting customer purchase behavior. Practical implications - The study concludes that the proposed approach would be beneficial for enterprises in terms of designing customized services and one-to-one marketing strategies. Originality/value - This study is the first attempt to adopt a hybrid approach combining individual-level and segment-based approaches to predict customers' individual purchase behaviors.
Citation Count: 3
Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market
(Emerald Group Publishing Ltd, 2022) Macakoglu, Sevval Seray; Cinar, Burcu Alakus; Peker, Serhat; Software Engineering
Purpose In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets. Design/methodology/approach Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score. Findings The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability. Practical implications The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices. Originality/value This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.
Citation Count: 0
A Methodology for Product Segmentation Using Sale Transactions
(Ieee, 2018) Peker, Serhat; Kocyigit, Altan; Eren, P. Erhan; Software Engineering
This paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the pro-posed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies.
Citation Count: 3
A methodology for product segmentation using sale transactions
(Institute of Electrical and Electronics Engineers Inc., 2018) Peker,S.; Kocyigit,A.; Erhan Eren,P.; Software Engineering
This paper presents a novel methodology for product segmentation using customers' transactions on products. The proposed methodology introduces FMC model, and utilizes this model's features and clustering algorithms to group products into segments. The applicability of the proposed approach has been demonstrated on data collected by a supermarket chain. The results show that the proposed methodology provides an efficient tool that can be used to identify different product segments and to gain valuable insights about these distinct groups. The resulting product segments can help managers in the inventory management and developing marketing strategies. © 2018 Croatian Society MIPRO.
Citation Count: 2
Prioritizing Use Cases for Development of Mobile Apps Using AHP: A Case Study in To-Do List Apps
(Springer, 2019) Yildirim,O.; Peker,S.; Software Engineering
With the rapid development of communication technologies, the uses of mobile apps have increased in a significant manner over the past few years. Every day many different types of mobile apps are uploaded to mobile application markets. However, it is very difficult for the apps to stay competitive and survive in these marketplaces. Covering the requirements fitting the needs of users is one of significant factors in mobile apps’ success in the market. In this regard, this study aims to use Analytic Hierarchy Process (AHP) to evaluate the use cases for the development of mobile apps. The results show that AHP provides an efficient tool which can be used to determine importance of the requirements in mobile apps considering users’ preferences. © 2019, Springer Nature Switzerland AG.
Citation Count: 0
Üniversite Web Sitesi Ana Sayfalarının KullanılabilirliğininDeğerlendirilmesi: Göz İzleme Yaklaşımı
(2021) Dalveren, Gonca Gökçe Menekşe; Peker, Serhat; Software Engineering; Information Systems Engineering
Ziyaretçilerin ilk karşılandığı yer olan üniversite web sitesi ana sayfalarının iyi bir tasarıma sahip olması ve ziyaretçileri tarafındankullanışlı bulunması, aday öğrenci, araştırmacı, yerli ve yabancı akademik kuruluşlar gibi dış paydaşların üniversiteye olan ilgileriniartırmak ve sürekli kılmak adına oldukça kilit rol oynamaktadır. Bu motivasyondan yola çıkarak bu çalışma, seçilen beş Türküniversitesinin ana sayfa tasarımlarını kullanılabilirlik yönünden değerlendirmeyi amaçlamaktadır. Bu amaç doğrultusunda göz izlemeyaklaşımı kullanılmış, bir insan-bilgisayar etkileşimi laboratuvarındaki göz izleme cihazı ve diğer donanımlar vasıtasıyla katılımcılarınilgili web sayfalarıyla olan etkileşimlerini incelenmiştir. Deneklerin görüntüleme davranış verilerinin belirli göz hareketlerine gözizleme cihazı yazılımı kullanılarak odaklanma sayısı, ilk ziyarete kadar geçen süre, ve toplam ziyaret süresi olarak sınıflandırılmasındansonra, ANOVA methodu ile istatistiksel detaylı analizi yapılmıştır. Elde edilen bulgular, kullanıcıların ilgili sayfalardaki menübileşenlerini bulma görevlerini yerine getirdikleri halde, bu sayfaların kullanılabilirliğinde istenilen bileşeni kısa sürede farkedememe,farklı alanlarda arama gibi zorluklarla karşılaştıklarını göstermiştir. Bu çalışma, bu alanda çalışan araştırmacılara, üniversite websayfalarının göz izleme yöntemi ile kullanılabilirliklerinin değerlendirmesine ilişkin değerli bir referans olmasının yanı sıra paydaşlarınilgisini çeken ve daha kullanıcı merkezli üniversite web sitesi ana sayfalarının tasarlanması yönünde çıkarımlar sunmaktadır.
Citation Count: 10
The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review
(Institute of Electrical and Electronics Engineers Inc., 2019) Öney,M.U.; Peker,S.; Software Engineering
Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs. © 2018 IEEE.
Citation Count: 10
The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review
(Institute of Electrical and Electronics Engineers Inc., 2019) Öney,M.U.; Peker,S.; Software Engineering
Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs. © 2018 IEEE.
Citation Count: 0
The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review
(Ieee, 2018) Oney, Mehmet Ugur; Peker, Serhat; Software Engineering
Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs.