Browsing by Author "Peker,S."
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Conference Object Citation Count: 6An empirical comparison of customer behavior modeling approaches for shopping list prediction(Institute of Electrical and Electronics Engineers Inc., 2018) Peker, Serhat; Kocyigit,A.; Erhan Eren,P.; Software EngineeringShopping 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.Conference Object Citation Count: 3A methodology for product segmentation using sale transactions(Institute of Electrical and Electronics Engineers Inc., 2018) Peker, Serhat; Kocyigit,A.; Erhan Eren,P.; Software EngineeringThis 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.Conference Object Citation Count: 2Prioritizing Use Cases for Development of Mobile Apps Using AHP: A Case Study in To-Do List Apps(Springer, 2019) Peker, Serhat; Peker,S.; Software EngineeringWith 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.Conference Object Citation Count: 10The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review(Institute of Electrical and Electronics Engineers Inc., 2019) Peker, Serhat; Peker,S.; Software EngineeringNetwork 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.Conference Object Citation Count: 10The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review(Institute of Electrical and Electronics Engineers Inc., 2019) Peker, Serhat; Öney,M.U.; Peker,S.; Software EngineeringNetwork 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.