Küçükaydın, Hande
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Name Variants
Küçükaydın, H.
Hande Küçükaydın
Küçükaydın, Hande
Hande Küçükaydın
Küçükaydın, Hande
Job Title
Email Address
kucukaydinh@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
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
0
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
0
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14LIFE BELOW WATER
0
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents
7
Citations
56
h-index
5
Documents
11
Citations
200
Scholarly Output
22
Articles
6
Views / Downloads
4433/23382
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
43
Scopus Citation Count
45
Patents
0
Projects
3
WoS Citations per Publication
1.95
Scopus Citations per Publication
2.05
Open Access Source
18
Supervised Theses
0
| 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 |
| International Transactions in Operational Research | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
22 results
Scholarly Output Search Results
Now showing 1 - 10 of 22
yl-bitirme-projesi.listelement.badge Suicide Tendency Classification and Suicide Number Prediction Forpopulation Subgroups(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ak, Mehmet; Küçükaydın, HandeSuicide is becoming a bigger problem for the world day by day and detecting population subgroups who are more prone to suicide is seen as one of the most important steps for taking precautions to decrease the suicide rates. This study consists of five machine learning models for suicide tendency classification and three machine learning models for prediction of suicide numbers by population subgroups. The dataset provided by World Health Organization is used in the project. Obtained models classify population subgroups as suicide-prone or less suicide prone with 86% accuracy and explain 90 % of the variance in the suicide number per 100,000 population of specific countries.Conference Object Combining Acceleration Techniques for Pricing in a Vrp With Time Windows(2016) Michelini, S; Arda, Y; Küçükaydın, Hande...yl-bitirme-projesi.listelement.badge 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.Article A Comparative Study of Branch-And Algorithms for Vehicle Routing With Time Windows and Waiting Time Costs(Wiley, 2026) Michelini, Stefano; Kucukaydin, Hande; Arda, YaseminBranch-and-price is one of the most commonly used methodologies for solving routing problems. In recent years, several studies have investigated advanced labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Such algorithms include efficient techniques such as decremental state space relaxation, ng-route relaxation, and several hybridizations of these two relaxation methods. In this study, we compare the performance of these labeling algorithms in a branch-and-price framework when applied to the vehicle routing problem with time windows and a variant of this problem in which waiting times have a linear cost. For the latter problem, we also propose an appropriate label structure with associated resource extension functions and dominance rules. We perform these comparisons by using a rigorous methodology, which consists of parameterizing several features of these algorithms, obtaining a good parameter configuration for each algorithm, and analyzing the performance of these configurations on benchmark instances. In order to obtain good configurations, we make use of irace, which is a tool for automated parameter tuning, while statistical tests are used for performance comparisons. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others.yl-bitirme-projesi.listelement.badge Forecasting With Ensemble Methods: an Application Using Fashion Retail Sales Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yüzbaşıoğlu, Orkun Berk; Küçükaydın, HandeIn this project, ensemble methods of machine learning are used to predict short term store sales of a fashion retailer. Sales forecasts of various products at different stores are generated for a span of three months with bagging tree regressor, random forest regressor, and gradient boosting regressor algorithm. Algorithms are trained and evaluated with real past sales data of a Turkish fashion retailer. The predictive performance of the models is compared with linear regression. The results of the study show that random forest regressor shows the best performanceyl-bitirme-projesi.listelement.badge Association Rule Mining on Ticket Sales Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Genç, Özge; Küçükaydın, HandeThis study aims to analyze the ticket sales data of a cultural institution and define association rules between the festivals/event group and festival/event group venues by market basket analysis. Market basket analysis is a well-known data mining method that is used to discover similarities between products or product groups. For market basket analysis, the apriori algorithm is applied which is considered as a popular data mining algorithm and helps to discover frequent item sets and forms association rules within the dataset. In this project, the apriori algorithm is applied using Python to discover the association rule: between the venues (implementation 1), between the venues excluding the venues used for a specific festival type (implementation 2), between festivals and event groups if any (implementation 3). According to the results of implementation 1, the associations are mostly between the venues of a specific festival type. According to the implementation 2, when we exclude this specific festival type from the dataset, it is seen the rules are mostly between the venues of another festival type. In implementation 3, when the festival venues variable is excluded and only the event names are considered, it is seen that the support, lift and confidence values are lower than the previous implementations.yl-bitirme-projesi.listelement.badge Hotel Recommendation for Online Travel Agencis(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kılıçlı, Cem; Küçükaydın, HandeSince the early 2000s, online travel agencies (OTAs) have become a central online market source, used by millions of users in all over the world. Recommendation systems became one of the essential tools for them to increase their profit.Conference Object Improved Business Model Representation of Innovation Concepts(World Conference on Technology, Innovation and Entrepreneurship, 2015) Dorantes-Gonzalez, Dante Jorge; Küçükaydın, Hande; Özlem, Şirin; Bulgan, Gökçe; Aydın, Utkun; Son Turan, Semen; Karamollaoğlu, Nazlı; Teixeira, Frederico FialhoExcept for academics and consultants, the concept and purpose of innovation (not to mention related concepts such as “Open Innovation", "Free-Intellectual Property Innovation," or "Open Source Innovation") is usually unclear for most entrepreneurs and other practitioners. It often times happens that newly coined terminology becomes misleading or may even include a certain degree of sensationalism, hence turning into a matter of debate for specialists in the realm of technology management. Such has been the case for the term “Open Innovation”, since the word “open” is mainly related to crowd sourced innovation, but not for the openness on intellectual property rights. Since innovation is about the commercialization of original ideas, so we propose a simple and visual business model setting to resolve these concepts. To this regard, the “Business Model Canvas” has been used in business and entrepreneurship to sketch and frame the key points behind the development of a startup. This model was suggested by Alexander Osterwalder (2008) in his work on Business Model Ontology, as a strategic analysis template for developing startups or documenting existing businesses. It describes the firm’s value proposition, partners, resources, activities, customer relationships, distribution channels, customers, revenue streams and cost structure. However, when it comes to innovative startups, this template does not explicitly include such significant innovation components as intellectual property, its alignment to strategies, and intellectual property flow. The present paper proposes the use of an improved version of the Business Model Canvas to originally represent different models of innovation like Open Innovation, thus providing a clear, visual and quick representation of their meaning, and consequently, contribute to a better understanding of different concepts of innovation.yl-bitirme-projesi.listelement.badge Churn Prediction of a Deal E-Commerce Website Customers(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Çevik, Müge; Küçükaydın, HandeToday, there is a lot of deal e-commerce sites which are essentially marketplaces. They provide deals which are offered by merchandisers. Because of the nature of these sites there is no subscription model; customers continue because of price or interest or quality not because of subscription. It is normal to have some customers who stop buying, which is defined by "churn". Data mining is now a new technique to define "churned" customers and to have prediction who will churn and what should be against. In this project customers are clustered via unsupervised clustering technique for clusters as "newly purchased", "frequently purchased" and "mostly payed" and "churned". Random Forest Classifier is used to prove that the "churned" customer clusters have homogeneous character and also it has been proved that the "churned" labelled customers have actually no deal order after the observed time period. To recommend what should be done to regain the churned customers to the site the deal order history of these customers have been explored and the deal categories from which they have bought have been found.Conference Object Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times(2016) Michelini, S; Arda, Y; Küçükaydın, Hande...
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