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Öğe An Additive FAHP Based Sentence Score Function for Text Summarization(Kaunas Univ Technology, 2017) Guran, Aysun; Uysal, Mitat; Ekinci, Yeliz; Guran, Celal BarkanThis study proposes a novel additive Fuzzy Analytical Hierarchy Process (FAHP) based sentence score function for Automatic Text Summarization (ATS), which is a method to handle growing amounts of textual data. ATS aims to reduce the size of a text while covering the important points in the text. For this aim, this study uses some sentence features, combines these features by an additive score function using some specific weights and produces a sentence score function. The weights of the features are determined by FAHP - specifically Fuzzy Extend Analysis (FEA), which allows the human involvement in the process, uses pair-wise comparisons, addresses uncertainty and allows a hierarchy composed of main features and sub-features. The sentences are ranked according to their score function values and the highest scored sentences are extracted to create summary documents. Performance evaluation is based on the sentence coverage among the summaries generated by human and the proposed method. In order to see the performance of the proposed system, two different Turkish datasets are used and as a performance measure, the F-measure is used. The proposed method is compared with a heuristic algorithm, namely Genetic Algorithm (GA). Resulting performance improvements show that the proposed model will be useful for both researchers and practitioners working in this research area.Öğe ANALİZİANALYSIS OF THE RESEARCH AND DEVELOPMENT EFFICIENCIES OF EUROPEAN UNION COUNTRIES(2017) Ekinci, Yeliz; Karadayı, Melis AlmulaÜlkelerin Araştırma ve Geliştirme (Ar-Ge) faaliyetleri gelişmekte olan pazarda rekabet edebilmek adına büyük önem taşımaktadır. Bu önem yaygın olarak kabul edilmesine rağmen, Ar-Ge faaliyetlerinin etkinliği literatürde nadir olarak incelenmiştir. Bu nedenle, bu çalışma Avrupa Birliği (AB) üyesi ülkelerin Ar-Ge verimliliğini incelemeyi amaçlamaktadır. Çalışma kapsamında, ülkeler arasındaki rekabetin çok yüksek olduğu AB ülkeleri seçilmiştir, ayrıca bu ülkeler Ar-Ge faaliyetlerine ciddi miktarda kaynak ayırmaktadırlar. Veri Zarflama Analizi (VZA) göreceli etk inlik sk orlarını ölçmek için k ullanılmıştır. Sonrasında, AB ülk elerinin siyasi ve düzenleyici ortamının Ar-Ge verimliliği üzerindeki etkisi hipotez testleri ile analiz edilmiştir. Göreceli etkinlik sk orları ve hipotez testlerinin sonuçları, sosyal politik a düzenleyiciler için Ar-Ge faaliyetlerinin planlanması k onusunda k arar vermede değerli bilgiler vermek tedir. Çalışmanın sonuçları ayrıca Türk iye gibi, AB'ye k atılmak isteyen ülkelere fayda sağlayacaktırÖğe Analizianalysis of the research and development efficıencies of european union countries(Uluslararası İşletme ve Yönetim Dergisi, 2017) Ekinci, Yeliz; Karadayı, Melis AlmulaResearch and Development (R&D) activities of the countries are of crucial importance in order to compete in the emerging market. Although this importance is widely recognized, the efficiency of these activities has been rarely examined in the literature. Therefore, this study is an attempt to analyze the R&D efficiencies of European Union (EU) member countries. EU countries are selected for this study since the competition between these countries is very high and they invest a significant amount of resources in this area. Data Envelopment Analysis (DEA) is used in order to measure the relative efficiency scores. Then, the effect of political and regulatory environment on R&D efficiencies of EU countries is analyzed via hypothesis testing. The relative efficiency scores and hypothesis test results give valuable information for social policy makers in making decisions about planning R&Öğe Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model(Elsevier, 2014) Ekinci, Yeliz; Ulengin, Fusun; Uray, Nimet; Ulengin, BurcThe general aim of this study is to provide a guide to the future marketing decisions of a firm, using a model to predict customer lifetime values. The proposed framework aims to eliminate the limitations and drawbacks of the majority of models encountered in the literature through a simple and industry-specific model with easily measurable and objective indicators. In addition, this model predicts the potential value of the current customers rather than measuring the current value, which has generally been used in the majority of previous studies. This study contributes to the literature by helping to make future marketing decisions via Markov decision processes for a company that offers several types of products. Another contribution is that the states for Markov decision processes are also generated using the predicted customer lifetime values where the prediction is realized by a regression-based model. Finally, a real world application of the proposed model is provided in the banking sector to show the empirical validity of the model. Therefore, we believe that the proposed framework and the developed model can guide both practitioners and researchers. (C) 2014 Elsevier B.V. All rights reserved.Öğe Analysis of Public Agenda during Covid-19 Pandemics Based on Turkish and English Tweets Using Nonnegative Matrix Factorization and Hypothesis Testing(Kauno Technologijos Universitetas, 2022) Ekinci, YelizAbstract: In this study, Turkish and English tweets through Twitter Application Program Interface (API) between 1-31 January 2021 are analyzed with respect to Covid-19. The collected tweets are preprocessed, labeled with the Vader Sentiment library, and then analyzed by topic modeling with Nonnegative Matrix Factorization. The analysis show that the most frequently mentioned word is “vaccine/aşı” after “Covid”. The topics modelled in the study are grouped into themes and the themes are seen to be similar in both languages, which means that the Turkish and world agenda are not very different in terms of themes in pandemics. Moreover, hypothesis tests are conducted to understand whether language and time period are related to sentiment class. The results show that the Turkish people are more neutral about the Covid-19 issue than other people in the world during the given period of time. Moreover, independent of the language, there are more negative and neutral tweets in the first half of January 2021, whereas there are more positive tweets in the second half of the month. To the best of our knowledge, this is the first study to analyze Covid-19 related tweets in two languages to compare the local and global agendas using topic modeling, sentiment analysis, and hypothesis testing methods. © 2022 Kauno Technologijos Universitetas. All rights reserved.Öğe A customer lifetime value model for the banking industry: a guide to marketing actions(Emerald Group Publishing Ltd, 2014) Ekinci, Yeliz; Uray, Nimet; Ulengin, FusunPurpose - The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry - namely the banking sector. Design/methodology/approach - This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as top 20 per cent and bottom 80 per cent profitable. Findings - Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also significant factors. Practical implications - Organisations in the banking sector have to persuade their customers to use certain routine risk-bearing transaction-based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product-related variables directly increase the value of customers. Originality/value - The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry-specific method which is based on easily measurable and objective indicators.Öğe Development of a hybrid model to plan segment based optimal promotion strategy(Sage Publications Ltd, 2023) Ekinci, Yeliz; Guran, AysunThe study addresses the long-term effects of promotions in terms of movement in a value-based segmentation (lead, iron, gold, platinum), instead of simply looking at response rates that occur shortly after the promotion. The study develops a framework for planning an optimal promotion strategy via Markov Decision Processes and Machine Learning methods for an online department store. In the first phase, the states are set as the customer profitability segments in order to conduct the MDPs. Then, MDP model is solved, and the optimal decision for each segment is determined. In the second phase, in order to aid the company for making their plans for the next year, the segment that the customer will belong to next year should be predicted. Prediction of the future customer profitability segment is performed by using several machine learning algorithms, and the best performing model is selected. Using this best performing model, the company can predict the future (potential) profitability segment of the customer and make plans which include the optimal promotions that will be directed to the customers depending on their segments (these optimal promotions are the outcomes of the first phase). The proposed framework can be applied by practitioners in e-commerce companies which keep customer data.Öğe Development of an early warning system for higher education institutions by predicting first-year student academic performance(Wiley, 2024) Cirak, Cem Recai; Akilli, Hakan; Ekinci, YelizIn this study, an early warning system predicting first-year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first-year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.Öğe Efficiency analysis of emergency departments in metropolitan areas(Elsevier Science Inc., 2020-03) Ekinci, YelizThe demand in the healthcare industry is increasing exponentially due to aging population of the world and this is leading to a rapid increase in the cost of healthcare. The emergency departments of the hospitals are the frontline of health care systems and play an additional critical role in providing an efficient and high-quality response for patients. The overcrowding at the emergency departments due to growing demand results in a situation where the demand for ED services exceeds the ability to provide care in a reasonable amount of time. This has led countries to reconsider their health policies in a way to increase their efficiency in their healthcare systems in general and in emergency departments, in particular. As in many countries, there has been a steady and significant increase in the number of patients that seek health services at the emergency departments of state hospitals of Turkey, due to the significant structural reforms in health services since 2003. While meeting this increasing demand, it is ever more important to provide these critical health services efficiently. Therefore, the efficiency of the emergency departments of seven general hospitals run by Istanbul's Beyoglu State Hospitals Association have been analyzed using categorical Data Envelopment Analysis (DEA) models. The analysis of DEA results is supported by a set of statistical methods to make it easier for the hospital administrators to interpret the analysis and draw conclusions. The analysis shows that less-equipped EDs are supported by better equipped, larger EDs, resulting in a hub-and-spoke type of structure among the EDs where "satellite" EDs serve an important referral function and thus evaluating their efficiency without taking the interoperability among these units into account would not be an accurate assessment of their performance.Öğe Evaluating R&D performance of EU countries using categorical DEA(Routledge Journals, Taylor & Francis Ltd, 2019) Karadayi, Melis Almula; Ekinci, YelizOver the coming decade, Research and Development (R&D) performance will be the key component of bringing innovation and the determinant of global competitiveness of nations. Therefore, this paper presents categorical Data Envelopment Analysis (DEA) for evaluating R&D performance of European Union (EU) countries. We utilise the output-oriented constant returns to scale (CRS) and variable returns to scale (VRS) DEA models with categorical data, namely, CAT-O-C and CAT-O-V models. In addition to DEA based framework, to examine the relationship between R&D performance and political-regulatory-economic situation of the countries; three research hypotheses are stated and their results are analysed. Policy implications about R&D activities can be derived for EU countries from the findings of this study.Öğe FACILITY LOCATION DECISION UNDER DEMAND UNCERTAINTY AND TRAVEL TIME FLUCTUATION(Univ Belgrade, Fac Transport And Traffic Engineering, 2015) Yildiz, Ecem; Ekinci, YelizThis study solves the facility location problem of an IT service company whose main problem is to arrive at the customer's site in the shortest time when support is needed. This problem should consider both the demand of the customers and the distance and/or travel time between the customers and the company. Travel times and demand are not constant since they are affected by many factors. Therefore, one should take into account uncertainty. This study formulates the problem by considering the demand as fuzzy and travel time as varying based on different time intervals, which are defined hourly, daily and seasonally. Moreover, we consider the cases of minimum speed, average speed and maximum speed for travel time. We illustrate the application of the proposed framework using data of a company. We compare the proposed framework with the traditional distance based optimization approach and show the advantages of the proposed method.Öğe Forecasting Preliminary Order Cost to Increase Order Management Performance: A Case Study in the Apparel Industry(IGI GLOBAL, 2022-08-26) Ekinci, YelizAbstract: In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN-based method are compared, and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of the authors’ knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.Öğe Fuzzy rule-based demand forecasting for dynamic pricing of a maritime company(Elsevier Science Bv, 2014) Cogun, Ozlem; Ekinci, Yeliz; Yanik, SedaIn this study, the pricing problem of a transportation service provider company is considered. Our goal is to find optimal prices by using probabilistic dynamic programming. A fuzzy IF-THEN-rule based system is used to identify the demand levels under different prices and other characteristics of the journey. The results obtained by optimal price policies show that the revenue increases by applying dynamic pricing policy instead of fixed pricing. Thus, the diversification of pricing policies under different conditions is beneficial for the company. (C) 2014 Elsevier B.V. All rights reserved.Öğe Intelligent Classification-Based Methods in Customer Profitability Modeling(Springer-Verlag Berlin, 2015) Ekinci, Yeliz; Duman, EkremThe expected profits from customers are important informations for the companies in giving acquisition/retention decisions and developing different strategies for different customer segments. Most of these decisions can be made through intelligent Customer Relationship Management (CRM) systems. We suggest embedding an intelligent Customer Profitability (CP) model in the CRM systems, in order to automatize the decisions that are based on CP values. Since one of the aims of CP analysis is to find out the most/least profitable customers, this paper proposes to evaluate the performances of the CP models based on the correct classification of customers into different profitability segments. Our study proposes predicting the segments of the customers directly with classification-based models and comparing the results with the traditional approach (value-based models) results. In this study, cost sensitive classification based models are used to predict the customer segments since misclassification of some segments are more important than others. For this aim, Classification and regression trees, Logistic regression and Chi-squared automatic interaction detector techniques are utilized. In order to compare the performance of the models, new performance measures are promoted, which are hit, capture and lift rates. It is seen that classification-based models outperform the previously used value-based models, which shows the proposed framework works out well.Öğe İstanbul için acil servis birimlerinin etkinliğinin kategorik veri zarflama analizi ile değerlendirilmesi(Journal of Yasar University, 2017) Karadayı, Melis Almula; Ekinci, Yeliz; Akkan, Can; Ülengin, FüsunÖzet: Son zamanlarda, Türkiye birçok ülkede olduğu gibi yüksek oranda kamu ve özel kaynaklarını sağlık sektörüne harcamaktadır. Bu nedenle sağlık sektöründe etkinlik analizi birçok paydaş için önem taşımaktadır. İstatistiksel göstergeler acil servis birimlerinin sağlık hizmeti yükünün çok önemli bir bölümünü üstlendiğini göstermiştir. Bu çalışma kapsamında Beyoğlu Kamu Hastaneleri Birliği Kapsamındaki hastanelerin acil servis birimlerinin etkinliği kategorik veri zarflama analizi (VZA) ile değerlendirilmiştir. Elde edilen sonuçlar ışığında sunulan sağlık hizmeti yönünden etkin olan ve olmayan acil servis birimleri tespit edilecektirÖğe İSTANBUL İÇİN ACİL SERVİS BİRİMLERİNİN ETKİNLİĞİNİN KATEGORİK VERİ ZARFLAMA ANALİZİ İLE DEĞERLENDİRİLMESİ(2017) Karadayı, Melis Almula; Akkan, Can; Ekinci, Yeliz; Ülengin, FüsunSon zamanlarda, Türkiye birçok ülkede olduğu gibi yüksek oranda kamu ve özel kaynaklarını sağlık sektörüne harcamaktadır. Bu nedenle sağlık sektöründe etkinlik analizi birçok paydaş için önem taşımaktadır. İstatistiksel göstergeler acil servis birimlerinin sağlık hizmeti yükünün çok önemli bir bölümünü üstlendiğini göstermiştir. Bu çalışma kapsamında Beyoğlu Kamu Hastaneleri Birliği Kapsamındaki hastanelerin acil servis birimlerinin etkinliği kategorik veri zarflama analizi (VZA) ile değerlendirilmiştir. Elde edilen sonuçlar ışığında sunulan sağlık hizmeti yönünden etkin olan ve olmayan acil servis birimleri tespit edilecektir.Öğe An MCDM-based game-theoretic approach for strategy selection in higher education(Elsevier Science Inc, 2022) Ekinci, Yeliz; Orbay, Benan Zeki; Karadayi, Melis AlmulaThis study proposes a framework for universities and governments to select strategies by considering the strategic interactions. The strategic choices of universities and governments can be determined by analyzing the related literature and discussing it with experts in higher education (HE). Because these experts form their evaluations depending on various criteria, the outputs of multi-criteria decision-making (MCDM) models are used to determine payoff values for players by considering all strategic combinations. After constructing the payoff matrix, the Nash equilibrium concept of game theory is used to determine optimal strategies for the universities and governments for simultaneously played games. Sequential versions of the games are also analyzed using backward induction. The results show that in all games constructed using criteria with different weights, either the government or the university, or both, preferred to motivate high-quality academic research. The proposed methodology can be used by the policymakers in the higher education area, both by the central planners (usually the government) and the universities.Öğe Optimal ATM replenishment policies under demand uncertainty(Springer Heidelberg, 2021) Ekinci, Yeliz; Serban, Nicoleta; Duman, EkremThe use of Automated Teller Machines (ATMs) has become increasingly popular throughout the world due to the widespread adoption of electronic financial transactions and better access to financial services in many countries. As the network of ATMs is becoming denser while the users are accessing them at a greater rate, the current financial institutions are faced with addressing inventory and replenishment optimal policies when managing a large number of ATMs. An excessive ATM replenishment will result in a large holding cost whereas an inadequate cash inventory will increase the frequency of the replenishments and the probability of stock-outs along with customer dissatisfaction. To facilitate informed decisions in ATM cash management, in this paper, we introduce an approach for optimal replenishment amounts to minimize the total costs of money holding and customer dissatisfaction by taking the replenishment costs into account including stock-outs. An important aspect of the replenishment strategy is that the future cash demands are not available at the time of planning. To account for uncertainties in unobserved future cash demands, we use prediction intervals instead of point predictions and solve the cash replenishment-planning problem using robust optimization with linear programming. We illustrate the application of the optimal ATM replenishment policy under future demand uncertainties using data consisting of daily cash withdrawals of 98 ATMs of a bank in Istanbul. We find that the optimization approach introduced in this paper results in significant reductions in costs as compared to common practice strategies.Öğe Optimization of ATM cash replenishment with group-demand forecasts(Pergamon-Elsevier Science Ltd, 2015) Ekinci, Yeliz; Lu, Jye-Chyi; Duman, EkremIn ATM cash replenishment banks want to use less resources (e.g., cash kept in ATMs, trucks for loading cash) for meeting fluctuated customer demands. Traditionally, forecasting procedures such as exponentially weighted moving average are applied to daily cash withdraws for individual ATMs. Then, the forecasted results are provided to optimization models for deciding the amount of cash and the trucking logistics schedules for replenishing cash to all ATMs. For some situations where individual ATM withdraws have so much variations (e.g., data collected from Istanbul ATMs) the traditional approaches do not work well. This article proposes grouping ATMs into nearby-location clusters and also optimizing the aggregates of daily cash withdraws (e.g., replenish every week instead of every day) in the forecasting process. Example studies show that this integrated forecasting and optimization procedure performs better for an objective in minimizing costs of replenishing cash, cash-interest charge and potential customer dissatisfaction. (C) 2014 Elsevier Ltd. All rights reserved.Öğe A SEGMENTATION BASED ANALYSIS FOR MEASURING CUSTOMER SATISFACTION IN MARITIME TRANSPORTATION(VILNIUS GEDIMINAS TECH UNIV,, 2018) Ekinci, Yeliz; Uray, Nimet; Uluengin, Füsun; Duran, CemThis study was conducted to profile customers according to the level of satisfaction with the service attributes of maritime public transport provided by Seabus Service Company (SSC), the sole provider of maritime transport in Istanbul. Such analysis needs to be conducted by considering market segments in terms of maritime transportation usage and post purchase behavior. This was accomplished by conducting quantitative research through face-to-face surveys of SSC passengers. According to the results by multivariate data analysis, including factor analysis and cluster analysis, six segments are revealed in terms of customer satisfaction level with the maritime service attributes. Moreover, there are significant differences among the segments in terms of usage frequency (travel frequency in this study), age and education level. Different strategies for different customer segments within the maritime passenger market to increase customer usage and satisfaction of maritime transportation in Istanbul are suggested from the findings. Thus, this paper provides guidelines for the Turkish Maritime Authorities as to how to expand maritime transportation usage in Istanbul, which is not only the largest city and the most crucial trade center of Turkey but also has the highest share of passenger maritime transportation in the country.
