Peker, Serhat

Profile Picture
Name Variants
S., Peker
Serhat, Peker
Peker, Serhat
Peker,S.
P., Serhat
P.,Serhat
S.,Peker
Job Title
Doktor Öğretim Üyesi
Email Address
serhat.peker@atilim.edu.tr
Main Affiliation
Software Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output

16

Articles

5

Citation Count

59

Supervised Theses

2

Filters

2017 - 2019112020 - 20225
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 10 of 16
  • Thumbnail Image
    Conference Object
    Citation - WoS: 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.
  • Thumbnail Image
    Conference Object
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 5
    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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 7
    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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 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.
  • Thumbnail Image
    Master Thesis
    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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 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.
  • Thumbnail Image
    Master Thesis
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 0
    Citation - Scopus: 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.