Özlük, Özgür
Loading...
Profile URL
Name Variants
Özgür Özlük
Job Title
Email Address
ozluko@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
SDG data is not available
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
24
Articles
3
Views / Downloads
2/2
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
Scopus Quartile Distribution
Competency Cloud
24 results
Scholarly Output Search Results
Now showing 1 - 10 of 24
Master Thesis Predicting Facebook Ad Impressions & Cpm Values(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekten, Semih; Özlük, ÖzgürIt is estimated that there are more than two billion active users on Facebook as of the first quarter of 2018 and social media has tremendous opportunities for advertisers in terms of performance and measurability. However, for marketing managers, it is very difficult to manage all the campaigns on different marketing channels and optimize for better results.For that reason, Facebook Marketing Partners or other optimization solutions emerged in the adtech market. In order to improve existing optimization solutions in the market, ad impression costs will be predicted in this study by using different machine learning techniques and different algorithms. The main goal of this study is to generate a robust model for predicting CPM values on Facebook, and to use that model as an in put for the existing optimization solution Adphorus offers for its clients. Adphorus is one of the Facebook Marketing Partners in the market.Master Thesis Sentiment Analysis of Hürriyet Emlak(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Korkmaz, Alev; Özlük, ÖzgürSentiment analysis refer to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, whether the attitude behind a text is positive, negative or neutral.Master Thesis Steel Product Clustering and Feature-Based Product Price Estimation for Flat Secondary Materials(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kemerci, Meryem; Özlük, ÖzgürMachine Learning replaces manual and repeatable processes every day, none of the industries can resist these developments. Older systems were rule-based which would bring some level of automation, but all had their limits. One of the goals of Machine Learning is prediction, and it can be used to obtain higher accuracy and better forecasts. Price predictions are made by hand according to market expectations and countries’ conjuncture in the past, but it is changing fast with the developments of Artificial Intelligence tools. In steel Industry, price levels are determining based on human intuition and simpler statistics. Profits are directly connected to the right pricing for the right time, machine learning algorithms may do the quotation of the steel properly to increase the company profits. This study aims to classify items as per quality and estimate the price level for the products. Feature selection preprocessing steps are used to enhance the performance and scalability of the classification method. The second part is feature-based product price estimation for the secondary products and selects the predictors of each quality under the product family.Master Thesis Big Data Analysis on Hotel Reviews(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Demir, Burcu; Özgür ÖzlükThis analysis aims to get a regression model of the reviews and the score by the guests to observe the effects of the content of the reviews on scores. The content of the reviews is also suitable for a sentiment analysis. These analyses are useful indicators of the hotel sector to catch the market direction positively. In this analysis, clustering hotel-based reviews and customer segmentation based on the reviews will be the key point. Nationality of the guests will be helpful information of the guests to get them into the segmentation pool. The guest who wants to stay in the best hotel in Europe while their trip could choose the best hotel. They can conclude that selection by meeting their needs.Master Thesis Text Classification Using Apache Spark(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Azizoğlu, Umut Rezan; Özlük, ÖzgürOne of the biggest problems of enterprises which are marketplace e-commerce business model with social platform; The improper communication of their social platform is the negative impact of the customer experience and the damage of the brand's value both materially and morally. As the number of daily commentaries is in numbers that cannot be read manually with optimal human resources in terms of company profitability, the interpretation modules in social market places are left unconscious. With this Project; established a model that prevents sentences that spoil the customer experience in their social platforms. Both data preparation and machine learning model were developed on Databricks notebook, using the apache spark platform with SparkML libraries and Pyspark language. The “Text Classification” approach is adopted when determining the model.Article Citation - WoS: 9Citation - Scopus: 9Sequential Testing in Batches(2017) Ünlüyurt, Tonguc; Shahmoradi, Zahed; Özluk, Özgur; Selcuk, Barış; Daldal, RebiWe study a new extension of the Sequential Testing problem with a modified cost structure that allows performing of some tests in batches. As in the Sequential Testing problem, we assume a certain dependence between the test results and the conclusion. Namely, we stop testing once a positive result is obtained or all tests are negative. Our extension, motivated by health care applications, considers a fixed cost associated with executing a batch of tests, with the general notion that the more tests are performed in batches, the smaller the total contribution of fixed costs to the sequential testing process. The goal is to minimize the expected cost of testing by finding the optimal choice and sequence of the batches available. The resulting NP-hard model is a variation of the set partitioning problem. We propose various heuristic algorithms for the effective solution of the problem and then demonstrate the performances of the algorithms through extensive numerical experiments.Master Thesis Segmentation With Unsupervised Learning: an Application Using the Walker's Data(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Polat, Taylan; Özgür ÖzlükIn this project, the Walkers suitable for the service were filtered by using the dataset shared by the DogGo company. Then, unsupervised machine learning methods such as K-Means, Gaussian, Principal Component Analysis were used to score and cluster the most suitable walkers according to performance, willingness, and experience.DogGo is the first mobile application in Turkey that provides pet walking and grooming services to its customers in a safe and professional manner. DogGo provides a professional service where dogs are taken care of in dog families' own homes or at the caretaker's home for any need of dog families. DogGo Company wants to provide the best matching of walkers and animals, using Machine Learning algorithms, through a 5-step acquisition process for their walkers.While the results of the K-means models created on the unique sliders were compared with the help of the Elbow method and the Silhouette score, the results of the Gaussian models were compared with the AIC and BIC method. In addition, an RFM scoring in a classical structure has also been created. When the results of the study were examined considering the Elbow and Silhouette scores, it was shown that the model created with K-Means gave the best results, and the number of clusters was decided as 2.Master Thesis Airbnb Host Recommendation Engine(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, Batuhan; Özgür ÖzlükIn this project, a fifth rule is proposed to reveal guests ' comments about hosts using the recommendation system and sentiment analysis for the super hosts' selection for Airbnb. This project is aimed to contribute to Airbnb's selection of Super hosts. In this study, sentiment analysis and comment data are examined, and polarity scores are created for use in suggestion systems. A collaborative filtering method is used for the recommendation system. The FunkSVD algorithm received the best RMSE score. Polarity scores are estimated for each latent user by looking at the host and listing id. The recommendation system developed ranked the polarity scores of hosts for each user.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 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.
- «
- 1 (current)
- 2
- 3
- »