Küçükaydın, Hande
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Küçükaydın, H.
Hande Küçükaydın
Küçükaydın, Hande
Hande Küçükaydın
Küçükaydın, Hande
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Email Address
kucukaydinh@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
1
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
Documents
6
Citations
55
h-index
5
Documents
10
Citations
200
Scholarly Output
21
Articles
5
Views / Downloads
4385/22341
Supervised MSc Theses
12
Supervised PhD Theses
0
WoS Citation Count
43
Scopus Citation Count
44
WoS h-index
4
Scopus h-index
4
Patents
0
Projects
3
WoS Citations per Publication
2.05
Scopus Citations per Publication
2.10
Open Access Source
17
Supervised Theses
12
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| Journal | Count |
|---|---|
| 2015 World Conference on Technology, Innovation and Entrepreneurship | 1 |
| 29th Meeting of Belgian Operational Research Society,Louvain-La-Neuve | 1 |
| Computers & Industrial Engineering | 1 |
| Euro | 1 |
| Journal of the Faculty of Engineering and Architecture of Gazi University | 1 |
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Now showing 1 - 10 of 21
Article Citation - WoS: 1Citation - Scopus: 1Determining and Evaluating New Store Locations Using Remote Sensing and Machine Learning(Tübitak, 2021) Ünsalan, Cem; Turgay, Zeynep Zerrin; Küçükaydın, Hande; Höke, BerkanDecision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model, which calculates store revenues, shouldbe rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques, which provide a novel way for evaluating new store locations.Master Thesis Predicting Outcomes and Improving Game Models for Football Matches(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Göçer, Murat; Küçükaydın, HandeThis study is conducted to predict the results of the 2017/2018 English Premier League football matches and show the teams what they should pay attention to in order to win. In this study, classification algorithms are used and the algorithm that gives the best results is applied to real matches. After evaluating the results, some suggestions are made for similar future studies and for the teams to develop their game models.Book Part Citation - WoS: 12Citation - Scopus: 12Bilevel Models on the Competitive Facility Location Problem(Springer, 2017) Küçükaydın, Hande; Aras, NecatiFacility location and allocation problems have been a major area of research for decades, which has led to a vast and still growing literature. Although there are many variants of these problems, there exist two common features: finding the best locations for one or more facilities and allocating demand points to these facilities. A considerable number of studies assume a monopolistic viewpoint and formulate a mathematical model to optimize an objective function of a single decision maker. In contrast, competitive facility location (CFL) problem is based on the premise that there exist competition in the market among different firms. When one of the competing firms acts as the leader and the other firm, called the follower, reacts to the decision of the leader, a sequential-entry CFL problem is obtained, which gives rise to a Stackelberg type of game between two players. A successful and widely applied framework to formulate this type of CFL problems is bilevel programming (BP). In this chapter, the literature on BP models for CFL problems is reviewed, existing works are categorized with respect to defined criteria, and information is provided for each work.Master Thesis Scoring Neighborhoods for Locating Atm Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Yıldırım, Oğuzhan; Küçükaydın, HandeFacility location is a general problem that is important for many different sectors and it is even more important when building the facility costs too much. In this project we analyzed the neighborhoods of Turkey and built two different models to estimate the good and bad neighborhoods for locating an ATM, which has significant costs for banks to build one. We used demographic and socio-economic data of 4,504 neighborhoods in Turkey and built models using Linear Regression and Decision Tree techniques of Machine Learning to find the best neighborhoods for locating a new ATM for a new bank entering the market. We compared the results of two machine learning methods and the results showed that we can make successful predictions of the neighborhoods by using machine learning methods which are good to locate an ATM without classical optimization techniques that requires complex calculations and machine learning methods.Master Thesis Price Prediction Using Machine Learning Techniques: an Application To Vacation Rental Properties(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Ay, Oğuz; Hande KüçükaydınPricing is a subjective process that highly depends on person. There is no general rule to price a house. That is why there is both overpriced and underpriced rental houses in rental listings in websites such as AirBnB. In order to reduce the effect of subjective pricing, a general machine learning model is built in this project to make more objective price predictions.In the literature, there are different machine learning models to make numeric predictions. Physical features of houses are used as an input to make inferences about the price of a house. These machine learning models can identify the relations between features and the price and make the predictions with respect to features of a new listing house that has not been priced before.In this project, six different machine learning models are developed. These are linear regression, ridge regression, support vector regressor, random forest regressor, light gradient boosting machine regressor and extreme gradient boosting regressor. The performances of all models are compared, and the best model is selected for hyper-parameter tuning to make more accurate predictions.Master Thesis Predicting Customer Satisfaction Via Structed and Unstructured Data Using Classification and Regression(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Danışman, Efehan; Küçükaydın, HandeAccording to different studies, retaining existing customers is five or more times more costly than acquiring new ones. This study aim to understand what customers expect from an airline using machine techniques. Dataset is scraped from Skytrax’s Airline Quality website and consists of 65947 observations with 17 columns consisting of one free format column that includes customer review. In order to do predict whether a customer recommends an airline or not, we try to utilize classification and regression algorithms. In addition to insights, this study also aims to compare the performance of the models and viability of using only free text in order to predict customer satisfaction.Master Thesis Ad Click Prediction Using Machine Learning Algorithms(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Uncu, Nazlı Tuğçe; Hande KüçükaydınOnline advertising has a great potential to boost business’ revenue. One of the key metrics that defines the success of online ad campaigns is click through rate (CTR) which indicates the total number of clicks received in relation to the total impression. Therefore, the click prediction systems, which have the aim of increasing the click through rates of online advertising campaigns by predicting the clicks, have become essential for businesses. For this reason, predicting whether an advertisement will receive a click fromthe user or not attracts the attention of researchers from the both industry and academia. In this capstone project, the click prediction is studied by using Avazu’s click logs dataset. The effects of having high cardinality categorical features and imbalanced data are examined during data preprocessing phase and then relevant features are selected to be used in modeling. The methods that are used for this classification problem are decision trees, random forest, k-nearest neighbor, extreme gradient boosting, and logistic regression. According to the results of the study, extreme gradient boosting shows the best performance.Conference Object Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times(2016) Michelini, S; Arda, Y; Küçükaydın, Hande...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.Conference Object Combining Acceleration Techniques for Pricing in a Vrp With Time Windows(2016) Michelini, S; Arda, Y; Küçükaydın, Hande...
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