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Öğe BSC-Based digital transformation strategy selection and sensitivity analysis(Mdpi, 2024) Oner, Mahir; Cebeci, Ufuk; Doğan, OnurIn today's digital age, businesses are tasked with adapting to rapidly advancing technology. This transformation is far from simple, with many companies facing difficulties navigating new technological trends. This paper highlights a key segment of a comprehensive strategic model developed to address this challenge. The model integrates various planning and decision-making tools, such as the Balanced Scorecard (BSC), Objectives and Key Results (OKR), SWOT analysis, TOWS, and the Spherical Fuzzy Analytic Hierarchy Process (SFAHP). Integrating these tools in the proposed model provides businesses with a well-rounded pathway to manage digital transformation. The model considers human elements, uncertainty management, needs prioritization, and flexibility, aiming to find the optimal balance between theory and practical applications in real-world business scenarios. This particular study delves into the use of SFAHP, specifically addressing the challenge of effectively selecting the most suitable strategy among various options. This approach not only brings a new perspective to digital transformation but also highlights the importance of choosing the right strategy. This choice is crucial for the overall adaptation of businesses. It shows how carefully applying the SFAHP method is key. Combining this with a successful digital transformation strategy is essential. Together, they provide practical and efficient solutions for businesses in a fast-changing technological environment.Öğe Business analytics in customer lifetime value: an overview analysis(Wiley Periodicals, Inc, 2025) Doğan, Onur; Hızıroğlu, Abdulkadir; Pisirgen, Ali; Seymen, Omer FarukIn customer-oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision-making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state-of-the-art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan-one of the study's significant contributions-was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.Öğe Cart-state-aware discovery of e-commerce visitor journeys with process mining(MDPI, 2024) Topaloglu, Bilal; Oztaysi, Basar; Doğan, OnurUnderstanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success.Öğe Catastrophic household expenditure for healthcare in Turkey: Clustering analysis of categorical data(MDPI, 2019) Doğan, Onur; Kaya, Gizem; Kaya, Aycan; Beyhan, HidayetThe amount of health expenditure at the household level is one of the most basic indicators of development in countries. In many countries, health expenditure increases relative to national income. If out-of-pocket health spending is higher than the income or too high, this indicates an economical alarm that causes a lower life standard, called catastrophic health expenditure. Catastrophic expenditure may be affected by many factors such as household type, property status, smoking and drinking alcohol habits, being active in sports, and having private health insurance. The study aims to investigate households with respect to catastrophic health expenditure by the clustering method. Clustering enables one to see the main similarity and difference between the groups. The results show that there are significant and interesting differences between the five groups. C4 households earn more but spend less money on health problems by the rate of 3.10% because people who do physical exercises regularly have fewer health problems. A household with a family with one adult, landlord and three people in total (mother or father and two children) in the cluster C5 earns much money and spends large amounts for health expenses than other clusters. C1 households with elementary families with three children, and who do not pay rent although they are not landlords have the highest catastrophic health expenditure. Households in C3 have a rate of 3.83% health expenditure rate on average, which is higher than other clusters. Households in the cluster C2 make the most catastrophic health expenditure.Öğe Categorization of customer complaints in food industry using machine learning approaches(2022) Kılınç, Deniz; Doğan, Onur; Bozyiğit, FatmaCustomer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.Öğe A comprehensive study of machine learning methods on diabetic retinopathy classification(Atlantis Press, 2021) Gürcan, Ömer Faruk; Beyca, Ömer Faruk; Doğan, OnurDiabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lower-dimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes. (C) 2021 The Authors. Published by Atlantis Press B.V.Öğe Continuous intuitionistic Fuzzy AHP & CODAS methodology for automation degree selection(Old City Publishing Inc, 2024) Alkan, Nursah; Otay, Irem; Gul, Alize Yaprak; Demir, Zeynep Burcu Kizilkan; Doğan, OnurThe automotive industry's evolution thrives on technological innovation, prioritizing efficiency, safety, and sustainability. Recent improvements in autonomous driving and IoT integration have revolutionized vehicle design, safety, and maintenance with different automation degrees from partial human control to full automation. Selecting these automation degrees involves complicated Multi-Criteria Decision-Making (MCDM) encompassing technical feasibility, societal impact, and regulatory compliance. Utilizing Analytic Hierarchy Process (AHP) and Combinative Distance-Based Assessment (CODAS) offers a structured framework to navigate these complexities. AHP establishes criteria importance, while CODAS handles uncertainties, enabling informed decisions balancing technology with ethical, societal, and regulatory considerations. Fuzzy extensions further refine these methodologies, empowering the industry to adeptly address subjective perceptions and ambiguous data, enhancing the decision-making framework for automotive technology evolution. This paper navigates the intricate landscape of automation degree selection within the automotive industry evolution, employing a structured approach merging fuzzy AHP and fuzzy CODAS methods by utilizing Continuous Intuitionistic Fuzzy Set (CINFUS). This approach not only brings a new perspective to autonomous vehicles but also highlights the importance of choosing the right automation degree. Moreover, a sensitivity analysis involved adjusting the weights assigned to different criteria within the Continuous Intuitionistic Fuzzy (CINFU) AHP framework. By systematically altering these weights and observing their impact on the final automation degree selection, decision-makers can understand the sensitivity of the chosen automation degree to changes in priority among criteria.Öğe A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method(Springer, 2020) Doğan, Onur; Deveci, Muhammet; Canitez, Fatih; Kahraman, CengizAutonomous vehicles (AVs) provide a new mobility option and have been becoming widespread around the world. They can be used not only as automobiles but also as public transport vehicles in the form of shuttles, buses and pods. One of the problem areas in implementing AVs as a public transport vehicle is to choose a suitable road for operating them. A fuzzy decision model combining analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) techniques with intuitionistic fuzzy sets is used to solve this problem. The results show that the BRT corridor is the most suitable option for operating AVs according to the decision criteria. Furthermore, operating AVs on a BRT corridor provides an unprecedented service provision approach.Öğe Customer churn prediction using deep learning(Springer Science and Business Media Deutschland GmbH, 2021) Seymen, Ömer Faruk; Doğan, Onur; Hızıroğlu, AbdülkadirChurn studies have been used for years to achieve profitability and to establish a sustainable customer-company relationship. Deep learning is one of the contemporary methods used in churn analysis due to its ability to process huge amounts of customer data. In this study, a deep learning model is proposed to predict whether customers in the retail industry will churn in the future. The model was compared with logistic regression and artificial neural network models, which are also frequently used in the churn prediction studies. The results of the models were compared with accuracy classification tools, which are precision, recall and AUC. The results showed that the deep learning model achieved better classification and prediction success than other compared models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Diagnosis of COVID-19 using deep CNNs and particle swarm optimization(Springer Science and Business Media Deutschland GmbH, 2022) Gürcan, Ömer Faruk; Atıcı, Uğur; Biçer, Mustafa Berkan; Doğan, OnurCoronavirus pandemic (COVID-19) is an infectious illness. A newly explored coronavirus caused it. Currently, more than 112 million verified cases of COVID-19, containing 2,4 million deaths, are reported to WHO (February 2021). Scientists are working to develop treatments. Early detection and treatment of COVID-19 are critical to fighting disease. Recently, automated systems, specifically deep learning-based models, address the COVID-19 diagnosis task. There are various ways to test COVID-19. Imaging technologies are widely available, and chest X-ray and computed tomography images are helpful. A publicly available dataset was used in this study, including chest X-ray images of normal, COVID-19, and viral pneumonia. Firstly, images were pre-processed. Three deep learning models, namely DarkNet-53, ResNet-18, and Xception, were used in feature extraction from images. The number of extracted features was decreased by Binary Particle Swarm Optimization. Lastly, features were classified using Logistic Regression, Support Vector Machine, and XGBoost. The maximum accuracy score is 99.7% in a multi-classification task. This study reveals that pre-trained deep learning models with a metaheuristic-based feature selection give robust results. The proposed model aims to help healthcare professionals in COVID-19 diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Enhancing e-business communication with a hybrid rule-based and extractive-based chatbot(MDPI, 2024) Doğan, Onur; Gurcan, Omer FarukE-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by AI and NLP technologies. This study focuses on developing a hybrid rule-based and extractive-based chatbot for e-business, which can handle both routine and complex inquiries, ensuring quick and accurate responses to improve communication problems. The rule-based QA method used in the chatbot demonstrated high precision and accuracy in providing answers to user queries. The rule-based approach achieved impressive 98% accuracy and 97% precision rates among 1684 queries. The extractive-based approach received positive feedback, with 91% of users rating it as good or excellent and an average user satisfaction score of 4.38. General user satisfaction was notably high, with an average Likert score of 4.29, and 54% of participants gave the highest score of 5. Communication time was significantly improved, as the chatbot reduced average response times to 41 s, compared to the previous 20-min average for inquiries.Öğe Exploration with process mining on how temperature change affects hospital emergency departments(Springer Science and Business Media Deutschland GmbH, 2021) Lull, Juan José; Doğan, Onur; Celda, Angeles; Mandingorra, Jesus; Lemus, Lenin; Pla, Miguel Ángel Mateo; Fernandez-Llatas, CarlosThe way patients are treated in Hospital Emergency Departments changes during the year, depending on many factors. One key component is weather temperature. Some seasonal maladies are tightly related to temperature, such as flu in cold weather or sunburn in hot weather. In this study, data from a hospital in Valencia was used to explore how harsh weather changes affect the emergency department, obtaining information about probable impacts of global warming effects in healthcare systems. Process mining techniques helped in the discovery of changes in the Emergency Departments. Some illnesses, such as heat stroke, are more prevalent during heatwaves, but more interestingly, the time to attend patients is also higher. Rapid changes in temperature are also analyzed through Process Mining techniques. © 2021, Springer Nature Switzerland AG.Öğe A framework for sustainable and data-driven smart campus(Scitepress, 2020) Köstepen, Zeynep Nur; Akkol, Ekin; Doğan, Onur; Bitim, Semih; Hızıroğlu, AbdülkadirAs small cities, university campuses contain many opportunities for smart city applications to increase service quality and use of public resources efficiency. Enabling technologies for Industry 4.0 play an important role in the goal of building a smart campus. The study contributes to the digital transformation process of.Izmir Bakircay University which is a newly established university in Turkey. The aim of the study is to plan a road map for establishing a smart and sustainable campus. A framework including an architectural structure and the application process, for the development of a smart campus have been revealed in the study. The system application is designed to be 3 stages. The system, which is planned to be built on the existing information systems of the university, includes data collection from sensors and data processing to support the management processes. The proposed framework expects to support some value-added operations such as increasing personnel productivity, increasing the quality of classroom training, reducing energy consumption, accelerating interpersonal communication and finding the fastest solution to the problems on campus. Therefore, not only a smart campus but also a system is designed for sustainability and maximum benefit from the facilities.Öğe From indoor paths to gender prediction with soft clustering(IOS Press, 2020) Doğan, Onur; Öztayşi, BaşarCustomer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes.Öğe Fuzzy association rule mining approach to identify e-commerce product association considering sales amount(Springer Heidelberg, 2022) Doğan, Onur; Kem, Furkan Can; Öztayşi, BaşarOnline stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers' choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.Öğe Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering(Springer Verlag, 2020) Doğan, Onur; Öztayşi, BaşarCustomer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths. © 2020, Springer Nature Switzerland AG.Öğe Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN(Pergamon-Elsevier Science Ltd, 2019) Doğan, Onur; Öztayşi, BaşarCompanies have an advantage over the competitors if they can present customized offers to customers. Demographic information of customers is critical for the companies to develop individualized systems. While current technologies make it easy to collect customer data, the main problem is that demographic data are usually incomplete. Hence, several methods are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy sets. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the research is to classify customer data into the gender classes using indoor paths. (C) 2019 Elsevier Ltd. All rights reserved.Öğe Individual behavior modeling with sensors using process mining(MDPI, 2019) Doğan, Onur; Martinez-Millana, Antonio; Rojas, Eric; Sepulveda, Marcos; Munoz-Gama, Jorge; Traver, Vicente; Fernandez-Llatas, CarlosUnderstanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.Öğe Müşteri memnuniyetinin süreç odaklı değerlendirilmesi: Bir çağrı merkezinde süreç madenciliği uygulaması(2021) Doğan, Onur; Ayyar, Başak; Çağıl, GültekinFirmaların varlıklarını sürdürebilmeleri için müşteri odaklı bir yaklaşım sergilemeleri büyük avantaj sağlamaktadır. Müşteriler artık kendisine sunulanı değil, beklentilerini karşılayan ürün veya hizmeti satın almaktadır. Bu sebepten dolayı firmalar müşterileri dinlemek, onların ihtiyaç ve beklentilerini karşılamak amacıyla çeşitli stratejiler izlemektedirler. Bu stratejik birimlerden birisi de firma ile müşteri arasında iletişimi sağlayan çağrı merkezleridir. Firmalar çağrı merkezleri ile müşterilerin sorunlarını çözerek memnuniyet düzeyini arttırmaya çalışırlar. Bununla birlikte, müşteri memnuniyetinin yönetilebilir, ölçülebilir ve karşılaştırılabilir olması için memnuniyet anketi uygulamaktadırlar. Bu çalışmanın amacı, müşteri memnuniyet anketlerinin de göz önüne alındığı süreç madenciliği yardımıyla müşteri memnuniyetinin değerlendirilmesidir. Firmanın IT sisteminde tutulan çağrı merkezi bölümüne ait 11 günlük (18 – 28 Mart 2019) tarihleri arasındaki 42185 olay günlüğü (event log) süreç madenciliği için kullanılmıştır. Öncelikle memnuniyet anketine katılan ve katılmayan müşterilerin sistem içindeki akışı çıkarılmıştır. Ardından ankete katılan müşteriler arasında 5 (çok memnunum) puan verenlerin ve diğer müşterilerin süreçleri incelenmiştir. Çalışmada ulaşılan sonuçlardan biri, çağrı sırasında bekletilen müşterilerin daha yüksek puan vermesidir. Bu sonuç müşterilerin bekletilmekten ziyade ihtiyacının karşılanmasına daha çok önem verdiğini göstermektedir. Bu açıdan çalışma, firmanın süreç odaklı yönetimine, süreç iyileştirme çalışmalarına, strateji çalışmalarına yol gösterebilecek özelliktedir.Öğe Natural gas consumption behavior of companies by clustering analysis(Pergamon-Elsevier Science Ltd, 2021) Doğan, OnurWe have still consumed natural gas as a restricted source of energy in our daily life. Moreover, the consumption of natural gas energy will continue to increase by the year. Although many studies have focused on electrical energy consumption, natural gas is another significant energy source that can be examined. Since companies consume much more gas, their gas consumption data are examined in this study. The study contributes to the literature by applying intuitionistic fuzzy c-means (IFCM) methodology to the natural gas industry. The main motivation and advantage of the methodology is two-fold. Because of its fuzziness, one data point can be assigned into more than one cluster, similar to real-world cases. Because of its intuitionistic side, it considers membership, non-membership and hesitant degrees. These two strengths of IFCM improves the clustering accuracy. IFCM clustering was used to arrange the companies with respect to the consumption amount to increase the understandability because 1049 companies' consumption data were collected. A calendar view was developed to visualize the consumption amounts in the clusters. The changes in consumption amounts were presented in different weather temperatures. Whereas some clustered companies were directly affected by temperature changes, others were not affected. The companies in the clusters were analyzed with respect to two main criteria: regularity and complexity. The findings showed while high levels in routine are related to manufacturing companies, high complexity level is an indicator of being active in the service industry.
