Güney, Evren
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Güney, Evren
Guney, E.
Evren Güney
Guney, E.
Evren Güney
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Email Address
guneye@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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WoS Researcher ID
Sustainable Development Goals
SDG data is not available
Documents
18
Citations
310
h-index
7
Documents
0
Citations
0
Scholarly Output
16
Articles
6
Views / Downloads
3554/5890
Supervised MSc Theses
8
Supervised PhD Theses
0
WoS Citation Count
70
Scopus Citation Count
80
WoS h-index
3
Scopus h-index
3
Patents
0
Projects
2
WoS Citations per Publication
4.38
Scopus Citations per Publication
5.00
Open Access Source
10
Supervised Theses
8
| Journal | Count |
|---|---|
| Information Sciences | 2 |
| Computers & Operations Research | 1 |
| European Journal of Operational Research | 1 |
| Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi | 1 |
| Iise Transactions | 1 |
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16 results
Scholarly Output Search Results
Now showing 1 - 10 of 16
Master Thesis Online Shopping Purchasing Prediction(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kazezyılmaz, İdil; Evren GüneyThis project aims to understand the purchasing behavior of the consumers and make predictions about purchasing according to website metrics such as page values, bounce rates.An existing dataset is used in this project. This dataset is available in the collection of data from an e-commerce website by Google Analytics, which consists of 10 numerical and 8 categorical attributes coming from 12,330 sessions. The 'Revenue' attribute is used as the class label. The attributes that have high impact on the prediction are; "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product-Related Duration". They represent the number of different types of pages visited by the visitor in that session and the total time spent in each of these page categories.The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by Google Analytics for each page in the e-commerce site. The "Special Day '' feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with a transaction.Since the purpose of this project is to predict potential purchasing using existing data, in the prediction part several machine learning algorithms such as decision trees, random forests will be applied to compare the models. The most suitable model will be chosen among these algorithms.Article Citation - WoS: 31Citation - Scopus: 33Large-Scale Influence Maximization Via Maximal Covering Location(Elsevier, 2020) Güney, Evren; Ruthmair, Mario; Sinnl, Markus; Leitner, MarkusInfluence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude.Master Thesis Music Generation Using Deep Learning Techniques(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Kutay; Evren GüneyThis project aims to generate songs using the Jukebox model and its architecture. Jukebox’s Vector Quantized Variational AutoEncoder (VQ-VAE) architecture is state-of-the-art deep generative model used for music generation and gives an outstanding result. For this purpose, different Elvis Presley songs were analyzed in audio domain using various Music Information Retrieval (MIR) methods. The top level of the Jukebox model was retrained with these songs in order to increase the quality of the songs that will be produced in the style of Elvis Presley. After that, 3 new samples were generated using the first six seconds of Elvis Presley - Jailhouse Rock as the input signal. At the end, these new songs were analyzed and compared.Article Citation - WoS: 2Citation - Scopus: 3Qubo Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Guney, Evren; Ehrenthal, Joachim; Hanne, ThomasThe Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.Master Thesis Retention Period Prediction for Pension Policies(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Bayır, Ömer; Güney, EvrenCustomer Retention in Pension market refers to the activities and actions companies and organizations take to reduce the number of customer defections. How long the customer will be with our company or will stay in the system is retention. There are already workings in my company and other companies in the market about customer retention. Existing works generally contains how to measure customer retention and how to define distribution channels are successful in customer retention. Also existing predictive models are working on the feature set customer fund, total collection, un-paid premium frequency in general. In pension market companies have small margin of profit from pension policies. To make a profit from pension policies the companies have to retain their customer for long years. It ‘s approximately nine year to make profit from a pension policy because of high sales costs. Therefore to gain a new customer is less profitable than retaining present customers in Pension Market. In my project, I want to look retention in the pension application phase of customer. My main purpose is when the customer applied for pension product predict its retention period. If I produce an applicable model, It will be used in my company’s sales channels.Article Citation - WoS: 26Citation - Scopus: 26An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks(Elsevier, 2019) Güney, EvrenThe influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.Conference Object Quantum Approaches To the 0/1 Multi-Knapsack Problem: Qubo Formulation, Penalty Parameter Characterization and Analysis(Science and Technology Publications, Lda, 2025) Güney, Evren; Ehrenthal, J.; Hanne, T.The 0/1 Multi-Knapsack Problem (MKP) is a combinatorial optimization problem with applications in lo gistics, finance, and resource management. Advances in quantum computing have enabled the exploration of problems like the 0/1 MKP through Quadratic Unconstrained Binary Optimization (QUBO) formulations. This work develops QUBO formulations for the 0/1 MKP, with a focus on optimizing penalty parameters for encoding constraints. Using simulation experiments across quantum platforms, we evaluate the feasibility of solving small-scale instances of the 0/1 MKP. The results provide insights into the challenges and opportuni ties associated with applying quantum optimization methods for constrained resource allocation problems. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.Master Thesis Internet Baking Channel Usage(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Aydemir, Büşra Nur; Güney, Evren…Master Thesis Customer Segmentation and Customer Churn Prediction for Babil.com(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çakar, Berk; Evren GüneyIn the past decade, a lot of players have joined into e-commerce market and competition in the market has been increasing lately. The e-commerce companies want to use their resources more efficiently to stay ahead in the competition. Personal communication with customers, increasing customer loyalty, acquiring new customers and preventing customer churn are some of the ways to achieve this goal.Babil.com is an e-retailer that sells books online and it is one of the companies which wants to stay ahead in the competition. It is founded in 2013 and now it is a 8 years old mature company. So, instead of spending much resources on acquiring new customers, trying to keep existing customers and increasing retention rate is a more ideal goal for the company. Also, personal communication with customers and reaching them with the right product in the right time is crucial.In this project, a customer segmentation with two levels is implemented to help Babil.com. For the first level of segmentation, customers’ value to company is identified by RFM segmentation and in the second level of segmentation customers’ behaviors are identified by K-Means clustering. To prevent customer churn, a machine learning algorithm which predicts customers who will churn in the next 6 months. With this algorithm, it will be easy to take an action for the right customers in the right time.Correction An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)(Elsevier, 2020) Güney, EvrenThe influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program thatapproximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently,we propose a linear programming relaxation based method with a provable worst casebound that converges to the current state-of-the-art 1 − 1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in termsof solution quality and this is one of the few studies that provides approximate optimalsolutions for certain real life social networks.
