Özlük, Özgür
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Özgür Özlük
Job Title
Email Address
ozluko@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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Turkish CoHE Profile ID
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Scholarly Output
24
Articles
3
Views / Downloads
5097/42646
Supervised MSc Theses
20
Supervised PhD Theses
0
WoS Citation Count
17
Scopus Citation Count
19
WoS h-index
2
Scopus h-index
3
Patents
0
Projects
1
WoS Citations per Publication
0.71
Scopus Citations per Publication
0.79
Open Access Source
21
Supervised Theses
20
| Journal | Count |
|---|---|
| Annals Of Operations Research | 1 |
| Journal of Public Procurement | 1 |
| Journal of the Operational Research Society | 1 |
| Proceedings of International Conference on Computers and Industrial Engineering, CIE | 1 |
Current Page: 1 / 1
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24 results
Scholarly Output Search Results
Now showing 1 - 10 of 24
Article Citation - WoS: 2Citation - Scopus: 5Increasing Procurement Efficiency Through Optimal E-Commerce Enablement Scheduling(Emerald Group Publishing Ltd., 2019) Özlük, Özgür; Cholette, Susan; Clark, Andrew GPurpose: This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings. Design/methodology/approach: This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints. Findings: The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions. Originality/value: The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period; additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.Master Thesis Employee Performance Prediction(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Sivas, Barış; Özgür ÖzlükDogGo is a company that aims to provide safe and professional dog walking and grooming services to dog owners through the mobile application. Thanks to the DogGo application, dog owners and people who is employee of company and wants to walk their dogs (to be called Walkers) can meet on the same platform on the mobile application interface. The problem was determined by company that they needed to be able to accurately predict the performance of the walkers in the upcoming dog-walker matches, thus ensuring the correct dog walker match. This study will be planned to serve to this company for calculating their current walkers’ performance in an accurate way. The relevant machine-learning model will first be based on the manual scoring system made by the company for the performance of existing employees, and then the model will be developed in the light of the gains obtained from this. For the performance of the model, the employees and their characteristics are important for the first time.Master Thesis Predicting the Reasonable Departments for the Human Resources Related Questions by Using the Text Classification Algorithms(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Sancı, Yavuz; Özlük, ÖzgürThe employees of Yapı Kredi Bank use a help desk system to ask their Human Resources related questions to the employees of the Human Resources departments. The questions are assigned automatically to the relevant departments by the system according to the subjects of the questions. In some cases, the mismatches between the contents and the subjects of the questions may cause the wrong Human Resources department assignments of the questions. Even though the application allows Human Resources employees to redirect the questions to the appropriate Human Resources departments, which are responsible for answering, the response time of these questions lasts longer. This project aims to analyze the content of the Human Resources related questions by using the text classification algorithms to predict the responsible Human Resources departments. Thus, it is aimed to respond to the questions in a much shorter time.Master Thesis Smart Precision Agriculture With Autonomous Irrigation System Using Rnn-Based Techniques(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Anuşlu, Timuçin; Özlük, ÖzgürThe study presents a solution to improve freshwater usage for irrigation in the agriculture by building a neural network model to predict soil moisture at 20 cm level with time series data over longer periods of time.Master Thesis Credit Card Fraud Detection Analysis and Machine Learning Application(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Meker, Tuğrul; Özlük, ÖzgürCredit card fraud transaction is a common term for theft and fraud action involving a payment card such as payment or credit card or debit card as a source of funds in transactions. With increased usage of POS channel or internet in recent years, the risks of credit card fraud have increased. Mostly, these illegal activities start with a compromise of data associated with the account number or important information that required to start the financial transaction. After, literature review and exploratory data analysis, machine learning algorithms are going to use to decide whether the transaction is fraud or not. Logistic regression, decision tree, Naive-Bayes, decision forest and linear SVC’s classifier algorithms are used in this study. With re-sampling choices (random-under, random-over sampling & SMOTE), these algorithms’ performances are compared. Logistic regression, decision tree, and random forest provide best results in terms of accuracy metrics. Grid-Search is applied to those three algorithms. Decision tree algorithm is chosen as the best algorithm for credit card fraud detection. Python 3.7 is used in this study.Master Thesis Rfm Based Customer Segmentation for a Mobile Application(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Baykan, Ozan Barış; Özgür ÖzlükIn this project, customer segmentation was made for Doggo, a mobile application that brings together trained dog walkers for people who are not able to provide daily needs of their dogs. The data was organized by obtaining the columns of recency, frequency, monetary and tenure, and RFM-based customer segmentation was made using machine learning algorithms such as K-means and Gaussian Mixture Model (GMM). Then, the model was built with the part of the dataset that includes recency, monetary and tenure columns using K-means. In addition, with a function developed, the RFM and tenure will be repeated at intervals determined by the Doggo operation team, and this tool is used to monitor the customer condition changing. Various marketing campaigns have been proposed according to the current situation and the transitions they have made.Master Thesis The Effect of Bert-Based Grammatical Analysis on Google Search Results(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Oğuz; Özgür ÖzlükThis study aims to study the BERT, namely Bidirectional Encoder Representations from Transformers model, which is introduced by Google and is of great importance in content analysis, and to examine the role of grammatical accuracy in the process of content quality measurement and Search Engine Results Pages (SERP). BERT has an important role among the algorithms used by Google in order to maintain the quality of search results and to provide more relevant content to users by understanding the content more effectively.In this study, CoLA data, which is accepted as the most reliable data in this field and therefore used frequently in similar BERT studies, is used. The main purpose here is to make a BERT-based grammatical evaluation of sentences in a content and then examine these results on pages with optimal ranking values, to examine the connection between search results and grammatical accuracy and the importance of this parameter.In this context, the project consists of two phases. In the first phase, the content of the pages that are visible in the first 20 in 50 different queries are scored with the pre-trained BERT model. In the second phase, a dataset that includes different SEO-focused metrics of the same pages is created manually, and the importance of the BERT score among these features is investigated.Master Thesis Tractor Sales Forecast Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, ÖzgürThis study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.Master Thesis Development and Comparison of Prediction Models for Estimating Short Term Energy Demand of a Hotel Building(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yılmaz, Selimcan; Özlük, ÖzgürThis project presents a machine learning model building approach to developing a model for predicting next hour electricity consumption of a hotel complex in Cyprus, with the aim of improving existing prediction accuracy due to comparing different models to choose best performing. Model building process in this project includes three main steps.Article Citation - WoS: 6Citation - Scopus: 5Optimal Keyword Bidding in Search-Based Advertising With Budget Constraint and Stochastic Ad Position(Taylor & Francis, 2019) Özlük, Özgür; Selçuk, Barış; Küçükaydın, HandeThis paper analyses the search-based advertising problem from an advertiser’s view point, and proposes optimal bid prices for a set of keywords targeted for the advertising campaign. The advertiser aims to maximise its expected potential revenue given a total budget constraint from a search-based advertising campaign. Optimal bid prices are formulated by considering various characteristics of the keywords such that the expected revenue from a keyword is a function of the ad’s position on the search page, and the ad position is a stochastic function of both the bid price and the competitive landscape for that keyword. We explore this problem analytically and numerically in an effort to generate important managerial insights for campaign setters.
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