Koç, Utku
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Koç U & Utku Koç & Koc U
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kocu@mef.edu.tr
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02.01. Department of Industrial Engineering
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Current Staff
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Sustainable Development Goals
1NO POVERTY
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Research Products
2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Scholarly Output
15
Articles
4
Views / Downloads
3464/37943
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
19
Scopus Citation Count
24
Patents
0
Projects
6
WoS Citations per Publication
1.27
Scopus Citations per Publication
1.60
Open Access Source
13
Supervised Theses
0
| Journal | Count |
|---|---|
| Computers & Industrial Engineering | 1 |
| Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi (online) | 1 |
| Operations Research Letters | 1 |
| Turkish Journal of Electrical Engineering & Computer Sciences | 1 |
Current Page: 1 / 1
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Competency Cloud
15 results
Scholarly Output Search Results
Now showing 1 - 10 of 15
Master Term Project Big Data Analytics on Used Car Information(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Demir, Efe; Utku KoçIn this research, a decision support system is implemented on a used car dataset. The main purpose is to predict the price information and reveal the related features. The price prediction problem is classified as a regression problem. The key point is to find the best-fitting model and obtain the best accurate prediction outcomes. Should we buy this car, or at what price may I sell my car? This work is about to answer these questions. Various regression models are compared, and detailed results are explained correspondingly.The constructed models will help customers to know about their car price and salability. And they can identify the buying opportunities. The percentage error approach which is detailed in the results section will be a guideline for customers/firms to make a market analysis or detect fraudulent listing information.Master Term Project Prediction of Credit Card Default(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Selçuk; Utku KoçAs profitable customer acquisition becomes more and more critical for the banking sector in terms of competition, the requirement to predict customer defaults with different machine learning algorithms is increasing. Thanks to similar practices, possible damages can be prevented. Due to the rapid change of machine learning with the changing technology, the fields of application and development in different sectors are also changing and developing rapidly. In this study, the aim is to make a comparison over model outcomes and making observations on outcomes to determine the areas that can be developed or researched with running different supervised and unsupervised machine learning algorithms on the final dataset gathered by doing following methods such as key points discovered in exploratory data analysis on an imbalanced credit card dataset, generating different features according to learned key points, eliminating imbalance with different oversampling and undersampling methods.Article Citation - WoS: 5Citation - Scopus: 6Generation of Feasible Integer Solutions on a Massively Parallel Computer Using the Feasibility Pump(Elsevier Science bv, 2017) Mehrotra, Sanjay; Koç, UtkuWe present an approach to parallelize generation of feasible mixed integer solutions of mixed integer linear programs in distributed memory high performance computing environments. This approach combines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FP instances with different starting solutions concurrently, while allowing them to share information. Our computational results suggest that the improvement resulting from parallelization using our approach is statistically significant. (C) 2017 Elsevier B.V. All rights reserved.Master Term Project Sms Spam Detection in Turkish Language(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Gürkan, Cem Kaya; Koç, UtkuShort message (SMS) is one of the most common communication methods. The growth of mobile phone users has led to a dramatic increase in using short messages. With the increasing number of mobile phone users, mobile phone users have started receiving unsolicited text messages. The use of SMS as a spam tool after the e-mail is due to a direct access to customer and high reversion to the users. These unsolicited short messages are disturbing the users even content intended for deceiving or defrauding (phishing). Up to date, all of the research carried out on SMS Spam detection was focused on the English language. In this study, Turkish datasets tagged with spam information is introduced and existing methods for English are applied to these datasets. The SMS dataset used in this study is gathered from different people and all messages are tagged according to whether they are spam or not. Naïve Bayes, Logistic Regression, SGD, SVM and Random Forest classification algorithms are tested with three feature extraction methods and a number of performance measures are evaluated. The evaluation resulted in a f-measure of 96.4% for SVM classification algorithm with TF-IDF (Term Frequency-Inverse Document Frequency) extraction method.Article Citation - WoS: 10Citation - Scopus: 12Coordination of Inbound and Outbound Transportation Schedules With the Production Schedule(Pergamon-Elsevier Science Ltd, 2016) Toptal, Aysegul; Sabuncuoglu, Ihsan; Koç, UtkuThis paper studies the coordination of production and shipment schedules for a single stage in the supply chain. The production scheduling problem at the facility is modeled as belonging to a single process. Jobs that are located at a distant origin are carried to this facility making use of a finite number of capacitated vehicles. These vehicles, which are initially stationed close to the origin, are also used for the return of the jobs upon completion of their processing. In the paper, a model is developed to find the schedules of the facility and the vehicles jointly, allowing for effective utilization of the vehicles both in the inbound and the outbound. The objective of the proposed model is to minimize the sum of transportation costs and inventory holding costs. Issues related to transportation such as travel times, vehicle capacities, and waiting limits are explicitly accounted for. Inventories of the unprocessed and processed jobs at the facility are penalized. The paper contributes to the literature on supply chain scheduling under transportation considerations by modeling a practically motivated problem, proving that it is strongly NP-Hard, and developing an analytical and a numerical investigation for its solution. In particular, properties of the solution space are explored, lower bounds are developed on the optimal costs of the general and the special cases, and a computationally-efficient heuristic is proposed for solving large-size instances. The qualities of the heuristic and the lower bounds are demonstrated over an extensive numerical analysis. (C) 2016 Elsevier Ltd. All rights reserved.Article Anomali Tespiti ve Suistimal Önleme: Telekomünikasyon Sektöründe Bir Uygulama(2025) Koç, Utku; Bulut, Özgür; Özalanyalı, ÖzgeBu çalışmada, telekom sektöründeki satış kanallarında ortaya çıkan anomalilerin tespitine ve suistimal olabilecek durumların engellenmesine yönelik istatistiksel bir yöntem geliştirilmiştir. Yöntemin geliştirilmesi ve test edilmesi sürecinde 371 farklı satış kanalına ait 9 aylık tüm satış bilgileriyle 340 binden fazla gerçek veri noktası kullanılmıştır. Anomali tespitinde en çok karşılaşılan engellerden biri yöntemin anomali olarak işaretlediği noktaların gerçekten anomali olup olmadığının teyit edilmesindeki zorluktur. Her bir kanalın kendi kontrol grubunu oluşturduğu bu çalışmada ise yöntemin anomali olarak işaretlediği noktaların gerçekten bir anomali olup olmadığı ilgili iş birimi tarafından değerlendirilmiş ve teyit edilmiştir. Her bir satış kanalı için günlük güven aralıkları ayrı ayrı hesaplanmış ve bu aralığın dışına çıkan durumlara hızlı tepki veren bir yöntem kullanılarak olası suistimallerin önüne geçilmiştir. Elde edilen bulgular, önerilen yöntemin anomali tespitinde başarılı olduğunu ve satış süreçlerindeki potansiyel suistimallerin önüne geçtiğini ve dolayısıyla müşteri memnuniyetini artırdığını göstermektedir. Geliştirilen yöntem yüksek performans ve ölçeklenebilirliği sağlamak için çoklu mimari yapısında uygulamaya alınmıştır. Geliştirilen yöntem ve uygulama, güvenlik ve veri bütünlüğü konularında da önemli avantajlar sunmaktadır. İlgili iş birimlerinin hızlı ve etkili kararlar alabilmesi, organizasyonun genel risk yönetimi stratejisine büyük katkı sağlamaktadır. Bu sayede, potansiyel tehditler zamanında tespit edilerek işletmenin güvenlik standartları korunmakta ve sürdürülebilir bir operasyonel çevre yaratılmaktadır. Ayrıca, projenin teknik yapısı anomali tespit sisteminin sürekli iyileştirilmesi hem yazılımın performansını artıracak hem de daha ileri düzeyde veri analizi imkanı sunacaktır. Sonuçlar, telekom şirketlerinin stratejik karar alma süreçlerine önemli katkılarda bulunarak rekabet avantajı sağlamalarına yardımcı olmaktadır.Master Term Project Predicting Birth Defects(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Korkut Özer, Selen; Koç, UtkuMany couples are eager to have a healthy baby. For this reason, the pregnant woman is trying to take their baby through the steps of adjusting their lives during the pregnancy, such as healthy nutrition, organic life, avoiding cosmetics. Even though the woman can do it, health problems can be observed in the baby at the time of birth or after birth. The causes of these health problems may be factors such as genetic, the physiological characteristics of the mother, environmental. In this paper, we tried to answer the question whether the health problems that occur in babies after childbirth can be estimated before birth. This includes the birth records of the American Centers for Disease Control and Prevention (CDC). Approximately 3M data was analyzed and the prediction model worked on the baby dataset. Boosting, Random Forest, Neural Network, Logistic Regression and SVM models were used to estimate the babies who could have any disease at birth. Sick babies were estimated with an accuracy of 69.5%.Master Term Project Predicting Customer Perfection on Brands Functional Near-Infrared Spectroscopy Measurements(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kemerci, Emre; Koç, UtkuCustomer perception on the brands have importance to give strategic decisions by marketing professionals. In classical ways, customer perception on brands are researched through conducting field surveys. Similarly, neuromarketing discipline have studies on customer behaviors, their perceptions, communication techniques etc. under the frame of decision-making process of human. In neuromarketing, functional near-infrared spectroscopy (fNIRS) is a technology used to measure oxy and deoxy hemoglobin concentration in the tissues in order to enable to analyze hemodynamic responses of the brain activities. In this study, a group of participants’ activations of prefrontal cortex so the hemodynamic responses that were collected against a set of stimuli, which is a brand logo and adjective associated with the brand is used as dataset. Measured hemodynamic response metrics are oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), total hemoglobin (HbT) and Oxygenation (Oxy) and the dataset includes 168 participants’ measurements for 30 stimuli. In addition, the information regarding the responses of the participants and common perception of stimuli (field study results for same stimuli) are also exists in dataset. The aim of the project is to predict through machine learning algorithms whether relation between brand and the relevant adjective is Positive, Negative or Neutral using these feature set. As methodology of this study, fNIRS measurements in the data is cleaned and Null values are handled, measurements are consolidated per participant and stimuli with two different method as feature creation and classification algorithms are used as supervised learning to predict brand perception. In conclusion, performance of support vector classifier and XGBoosting algorithms are become very low, slightly over 50% accuracy despite the optimization with different classifier parameters. Further studies are addressed as performing feature engineering studies with different options.Master Term Project Analyzing the Drivers of Customer Satisfaction Via Social Media(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yücel, Kadir Kutlu; Koç, UtkuSocial media became a great influence force during the last decade. Active social media user population increased with the new generations. Thus, data started to accumulate in tremendous amounts. Data accumulated through social media offers an opportunity to reach valuable insights and support business decisions. The aim of this project is to understand the drivers of customer satisfaction by public sentiments on Twitter towards a financial institution. Data was extracted from the most popular microblogging platform Twitter and sentiment analysis was performed. The unstructured data was classified by their sentiments with a lexicon-based model and a machine learning based model. The outcome of this study showed machine learning based model successfully overcame the language specific problems and was able to make better predictions where lexicon-based model struggled. Further analysis was performed on the extreme daily average sentiment scores to match these days with prominent events. The results showed that the public sentiment on Twitter is driven by three main themes; complaints related to services, advertisement campaigns, and influencers’ impact.Master Term Project Football Player Profiling Using Opta Match Event Data: Hierarchical Clustering(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kalenderoğlu, Uğurcan; Koç, UtkuIncreasing popularity of data analytics has impacted the sport industry. Dimension of available data and best practices on the usage of data analytics increased as a result of this trend. Player profiling is one of emerging hot topics among those, especially in football. On the other hand, income and expense balance of transfers has been biggest burden on clubs’ financials while it should be reverse. Scouting processes are currently dominated by bilateral relations and intuitive comments of scouting staff. It is an important step to transform into data driven decision framework to overcome this situation. It is crucial to replace a player who leave the team with someone who has potential and very close playing style. Player profiling is the first step to do this. The data set used in this project is obtained from Opta – a sport focused data company – and contains all actions performed on-ball at player level from Turkish Super League, English Premier League and German Bundesliga in three seasons between 2015 and 2018. Principal component analysis is applied to the dataset in order to reduce dimensionality to the 15 features which consists of 2469 players and 271 features at the beginning. As a result of this study, it is observed that there are twelve different player clusters within the traditional main positions; three for defenders, four for midfielders and five for forwards. Clubs can enrich and benefit from these clusters in three ways: 1) evaluation of a player style over a period of time and detecting the best role fit 2) analyzing the effect of cluster combination to decide which line-up yields better team results 3) finding the closest match to a player who is subject to replacement.