DSpace Arşivi :: by Yazar "Peker, Serhat" değerine göre listeleniyor

Yazar "Peker, Serhat" seçeneğine göre listele

Listeleniyor 1 - 20 / 22

A clustering approach for classifying scholars based on publication performance using bibliometric data
(Cairo Univ, Fac Computers & Information, 2024) Pisirgen, Ali; Peker, Serhat
This study introduces a clustering framework that effectively evaluate scholars' publication performance by utilizing cluster analysis and bibliometric data. In order to capture the various aspects of scholars' publication characteristics, our proposed framework integrates four distinct features, namely APIR which represents Academic age, Productivity, Impact, and Recency. The proposed framework is implemented in a case study focusing on Turkish academia, utilizing a dataset comprising 13,070 scholars from 24 diverse academic divisions across 30 Turkish universities. Cluster analysis yields seven groups of scholars with diverse publishing characteristic based on APIR features and these obtained clusters are profiled as freshmen, stagnant impactful mids, rising stars, stagnant and non-prolific juniors, stagnant impactful seniors, super stars, currently active and prolific seniors. To enhance the cluster analysis results, additional cross analysis is performed based on scholars' certain demographics such as affiliating institutes, divisions, academic titles, and PhD qualification. Scholars in clusters with superior publication performance are often affiliated with top-ranked universities and have academic backgrounds in the fields of Medicine, Engineering, and Natural Sciences. Practically, generated scholar segments and analysis based on these scholar profiles can serve as useful input for policy makers during having decisions about recruitment, promotion, awarding and allocation of funds.
A multidimensional data warehouse design to combat the health pandemics
(Springer International Publishing, 2022) Turcan, Gizem; Peker, Serhat
The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It appears that the simplest way to tackle this issue is to spread the digitalization efforts concerning all data-based applications. Given the significance of pandemic data management, it is essential to have a data warehouse that collects, associates, and communicates these data. Containing a significant volume of structured data, warehousing can provide the necessary foundation for data mining and the development of analytical tools. To this end, the present paper proposes a data warehouse for combatting and managing pandemics, with the possibility to be enhanced for other personal or public health-related initiatives. In this research, the bottom-up data warehouse building methodology is used to construct a warehouse. A fact constellation schema model is utilized to accommodate the information ranging from citizen demographics to physician-prescribed drugs and laboratory tests. Sample queries are executed based on the proposed data warehouse for different purposes, and desired query results are obtained within proper response times. The proposed data warehouse contributes to countrywide implementation of pandemic practices and illuminates research on faster, less expensive, and safer management of citywide, nationwide, or worldwide health emergencies within a robust technical framework by governments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.
Accessibility evaluation of university hospital websites in Turkey
(Springer Heidelberg, 2022) Macakoğlu, Şevval Seray; Peker, Serhat
Hospital web pages serve as an interactive communication resource to meet the needs of patients, healthcare professionals and other stakeholders. The aim of this study is to present the accessibility analysis of 58 university hospital websites in Turkey. For this purpose, websites of the selected university hospitals were analyzed using two different online automated testing tools. The results showed that university hospital websites in Turkey had low compliance levels according to the WCAG 2.0 guidelines. Most of the websites did not even meet the minimum requirements for compliance level A. In addition, almost all of the websites had broken links and about a third of the websites had problems with accessing from mobile devices. Moreover, some important clues that draw attention to the accessibility problems of websites are also discussed in this study. Hence, the results of this study provide feedback to developers and administrators to improve the accessibility of these websites.
Accessibility, usability, and security evaluation of universities' prospective student web pages: a comparative study of Europe, North America, and Oceania
(Springer Heidelberg, 2022) Macakoğlu, Şevval Seray; Peker, Serhat; Medeni, İhsan Tolga
Universities' prospective student web pages aim to disseminate information about their academic and social opportunities to their stakeholders; therefore, they must be accessible, of high quality of use and reliable. This article presents the accessibility, usage performance, and security analysis of prospective student web pages of 330 universities from three continents, namely Europe, North America, and Oceania. For this purpose, university websites were selected based on the Webometrics ranking, and online automated test tools were used. The results showed that websites at universities in North America paid more attention to accessibility and quality of use on prospective student web pages, followed by Oceanian and European websites. Evaluated websites had low compliance levels according to the WCAG 2.0 guideline. No major problems were identified in terms of usability and security, but there were certain points for improvement. Moreover, we present and discuss recommendations to developers and administrators for websites to resolve accessibility, usability, and security breaches and provide information equally to all stakeholders. Hence, this analysis report provides feedback to web developers to improve accessibility, quality of use, and security issues of university websites and their prospective student web pages.
Analysis of countries' performances in individual Olympic Games using cluster analysis and decision trees: the case of Tokyo 2020
(Emerald Group Publishing Ltd, 2024) Cetinkaya, Ahmet; Peker, Serhat; Kuvvetli, Umit
PurposeThe purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.Design/methodology/approachThis research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.FindingsThe study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.Research limitations/implicationsThe study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.Practical implicationsThe research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.Social implicationsThe research holds significant social implications by contributing insights that extend beyond the realm of sports.Originality/valueThe originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.
A combined approach for customer profiling in video on demand services using clustering and association rule mining
(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Güney, Sinem; Peker, Serhat; Turhan, Çiğdem
The purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: high consuming-valuable subscribers, less consuming subscribers,less consuming-loyal subscribers and disloyal subscribers. In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.
A comparison of neural network approaches for network intrusion detection
(Springer International Publishing Ag, 2020) Oney, Mehmet Uğur; Peker, Serhat
Nowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.
Designing a data warehouse for earthquake risk assessment of buildings: A case study for healthcare facilities
(2021) Özcan, Mert; Peker, Serhat
Since earthquake is one of the most dangerous natural phenomena, it is necessary to be prepared for the negative consequences of the earthquake in advance. It is very important that healthcare facilities must continue to provide service during and after an earthquake. Therefore, this study focuses on designing a data warehouse model for the earthquake risk assessment of healthcare facilities which are needed much more than other public buildings phys-ically. The proposed design utilizes a fact constellation schema model and take a public legislation containing principles regarding identification of risky buildings. This solution can provide a repository for data regarding earthquake risk assessment from different operational systems and play a key role in supporting critical decision-making process.
The effects of the content elements of online banner ads on visual attention: Evidence from an-eye-tracking study
(MDPI, 2021) Peker, Serhat; Menekse Dalveren, Gonca Gokce; İnal, Yavuz
The aim of this paper is to examine the influence of the content elements of online banner ads on customers' visual attention, and to evaluate the impacts of gender, discount rate and brand familiarity on this issue. An eye-tracking study with 34 participants (18 male and 16 female) was conducted, in which the participants were presented with eight types of online banner ads comprising three content elements-namely brand, discount rate and image-while their eye movements were recorded. The results showed that the image was the most attractive area among the three main content elements. Furthermore, the middle areas of the banners were noticed first, and areas located on the left side were mostly noticed earlier than those on the right side. The results also indicated that the discount areas of banners with higher discount rates were more attractive and eye-catching compared to those of banners with lower discount rates. In addition to these, the participants who were familiar with the brand mostly concentrated on the discount area, while those who were unfamiliar with the brand mostly paid attention to the image area. The findings from this study will assist marketers in creating more effective and efficient online banner ads that appeal to customers, ultimately fostering positive attitudes towards the advertisement.
Examining hotel characteristics and facilities influencing customer satisfaction using decision tree analysis
(Emerald Group Publishing Ltd, 2024) Pisirgen, Ali; Erdogan, Ali Mert; Peker, Serhat
PurposeThis study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.Design/methodology/approachThe research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.FindingsThe analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.Originality/valueThis study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.
Kiyaslio: a gamified mobile crowdsourcing application for tracking price dispersion in the grocery retail market
(Emerald Group Publishing Ltd, 2022) Macakoğlu, Şevval Seray; Çınar, Burcu Alakuş; Peker, Serhat
Purpose In the recent years, the rapid growth of the grocery retailing industry has created a great heterogeneity in prices across sellers in the market. Online price comparison agents which are key mechanisms to solve this problem by providing prices from different sellers. However, there are many sellers in the grocery industry do not offer online service, and so it is impossible to automatically retrieve price information from such grocery stores. In this manner, crowdsourcing can become an essential source of information by collecting current price data from shoppers. Therefore, this paper aims to propose Kiyaslio, a gamified mobile crowdsourcing application that provides price information of products from different grocery markets. Design/methodology/approach Kiyaslio has been developed through leveraging the power of crowdsourcing technology. Game elements have also been used to increase the willingness of users to contribute on price data entries. The proposed application is implemented using design science methodology, and it has been evaluated through usability testing by two well-known techniques which are the system usability scale and the net promoter score. Findings The results of the usability tests indicate that participants find Kiyaslio as functional, useful and easy to use. These findings prove its applicability and user acceptability. Practical implications The proposed platform supports crowd sourced data collection and could be effectively used as a tool to support shoppers to easily access current market product prices. Originality/value This paper presents a mobile application platform for tracking current prices in the grocery retail market whose strength is based on the crowdsourcing concept and incorporation of game elements.
Müşteri şikâyet yönetiminde firmaların performanslarının değerlendirilmesi: Kümeleme analizi incelemesi
(Dicle Üniversitesi, 2022) Ödev, Gamze; Peker, Serhat
Müşteri memnuniyetinde, hizmet ve ürünün kalitesi kadar müşteri şikayetlerinin dikkate alınması ve etkili bir şekilde yönetilmesi de oldukça önemli rol oynar. Günümüzde online ortamlarda şikayet daha fazla tercih edilmektedir. Bu çalışmanın amacı, kümeleme analizini kullanarak internet ortamında firmaların aldığı müşteri şikayetlerini ve bunları yönetim performanslarını değerlendirmektir. Bu amaca yönelik Sikayetvar.com internet sitesinden elde edilen veriler, CRISP-DM (Cross Industry Standard Process for Data Mining; Çapraz Endüstri Veri Madenciliği Standart Süreci) adımları baz alınarak iki aşamalı kümele analizi yöntemiyle analiz edilmiş ve elde edilen firma kümeleri profillenmiştir. Ayrıca elde edilen sonuçlar sektör bazlı olarak değerlendirilmiştir. Bu çalışmada önerilen yaklaşım ile firmalar şikayet yönetim performanslarını tespit edebilecek, diğer firmalar içindeki yerini görebilecek ve bu bağlamda başarılı firma profillerini baz alarak kendilerini geliştirebileceklerdir.
Predicting firms' performances in customer complaint management using machine learning techniques
(Springer International Publishing Ag, 2022) Peker, Serhat
With the globalization and more intense increasing competition, customer relationship management (CRM) is an important issue in today's business. In this manner, managing customer complaints which is a critical part of CRM presents firms with an is an opportunity to make long-lasting and profitable relationships with customers. In this context, the aim of this paper is to predict firms' performances in online customer complaint management using machine learning algorithms. This study utilizes data obtained from Turkey's largest and well-known third-party online complaint platform and employs three popular machine learning classifiers including decision tree (DT), random forests (RF) and support vector machines (SVM). The results show that the RF algorithm performed better in firms' performance prediction compared to other ML algorithms.
Predicting the Duration of Professional Tennis Matches Using MLR, CART, SVR and ANN Techniques
(Springer International Publishing Ag, 2024) Duen, Serdar; Peker, Serhat
This research aims to predict the duration of professional tennis matches by utilizing a dataset that includes player statistics, match characteristics and court attributes. Various machine learning techniques, such as multiple linear regression (MLR), classification and regression trees (CART), support vector regression (SVR) and artificial neural networks (ANN), are applied for this purpose. The study involves a comprehensive dataset spanning professional tournaments from 1993 to 2022. Separate predictive models were developed for tournaments played over 3 and 5 sets employing the corresponding ML techniques and their performances were compared. The findings revealed that the predictive models with MLR and SVR methods excel in best-of-3 set matches, while the ones with SVR and ANN exhibit superior performance for best-of-5 set matches. This research contributes valuable insights into the factors influencing match duration and aids in developing more effective predictive models for tennis events.
Preeclampsia prediction via machine learning: a systematic literature review
(Taylor & Francis Ltd, 2024) Ozcan, Mert; Peker, Serhat
Preeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. We reviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Na & iuml;ve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.
Prioritizing use cases for development of mobile apps using AHP: A case study in to-do list apps
(Springer, 2019) Yıldırım, Onur; Peker, Serhat
With the rapid development of communication technologies, the uses of mobile apps have increased in a significant manner over the past few years. Every day many different types of mobile apps are uploaded to mobile application markets. However, it is very difficult for the apps to stay competitive and survive in these marketplaces. Covering the requirements fitting the needs of users is one of significant factors in mobile apps’ success in the market. In this regard, this study aims to use Analytic Hierarchy Process (AHP) to evaluate the use cases for the development of mobile apps. The results show that AHP provides an efficient tool which can be used to determine importance of the requirements in mobile apps considering users’ preferences. © 2019, Springer Nature Switzerland AG.
Şirketlerin sürdürülebilirlik web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik perspektiflerinden kalite değerlendirmesi: Türkiye örneği
(Selçuk Burak HAŞIOĞLU, 2024) Yüksel, Sıla Azer; Peker, Serhat
Kurumsal sürdürülebilirlik web sayfaları, uzun vadeli iş sürekliliği için çok önemli olan çevresel etkiyi azaltma stratejilerini yansıtmaktadır. Bu bağlamda, bu sayfalarda erişilebilirlik, kullanılabilirlik ve güvenlik gibi kriterlerin sağlanması oldukça önemlidir. Bu makale, kurumsal sürdürülebilirlik web sayfalarının kalitesini erişilebilirlik, kullanılabilirlik ve güvenlik perspektiflerinden değerlendirmektedir. Örneklemimiz 71 Türk şirketinden oluşmaktadır ve analiz, TAW, GTmetrix, SUCURI, Google Mobile-Friendly ve Dead Link Checker otomatik çevrimiçi test araçları kullanılarak sürdürülebilirlik web sayfaları üzerinde gerçekleştirilmiştir. Sonuçlar, Türk şirketleri arasında sürdürülebilirlik web sayfalarının genel kalitesinin artırılması gerektiğini göstermektedir. Bu araştırmanın bulguları aynı zamanda temel sorunları ele almakta ve web yöneticileri ve geliştiricileri için yapıcı bir geri bildirim işlevi görerek onları bu web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik yönlerinde iyileştirmeler yapmaya yönlendirmektedir. Dolayısıyla bu araştırma, söz konusu web sayfalarının performansına ilişkin değerli bilgiler sunmayı ve böylece kurumsal sürdürülebilirlik iletişimi ve şeffaflığının ilerlemesine katkıda bulunmayı amaçlamaktadır.
Transactional data-based customer segmentation applying CRISP-DM methodology: A systematic review
(Springer International Publishing, 2023) Peker, Serhat; Kart, Özge
In recent years, as digital transformation picked up stream, the volume of customer transactional data that become available to companies has increased. By making use of such vast amount of transactional data and employing various data mining techniques, customer segmentation has received intensive attention from different industries, while significant research effort has been devoted to this topic, and the body of literature has begun to accumulate. In this context, the aim of this paper is to provide a comprehensive review of literature on transactional data-based customer segmentation to identify different characteristics in the field, analyze the application of data mining techniques, and highlight important points for further research. To review the existing literature in the field, three major online databases were used, and eventually, 84 relevant articles published in journals of well-known publishers are selected. The identified articles then completely analyzed based on the diverse criteria of the stages of CRISP-DM (CRoss Industry Standard Process for Data Mining) framework, and the results were reported. This systematic literature review can be very useful for academics and practitioners by providing a comprehensive overview of research work on customer segmentation using data mining and presenting guidelines for future research in this area as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
Türk üniversitelerinin aday öğrenci web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik açısından değerlendirilmesi
(2022) Medeni, Tunç Durmuş; Peker, Serhat; Medeni, İhsan Tolga; Macakoglu, Sevval Seray
Üniversitelerin aday öğrenci web sayfaları sosyal ve akademik anlamda paydaşlarına bilgi sağlama konusunda önemli bir iletişim kaynağıdır, bu sebeple bu sayfaların kullanımının kolay, güvenli ve erişilebilirlik standartlarına uygun olması beklenir. Bu makale, Türkiye’deki üniversitelerin aday öğrenci web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik değerlendirmesini sunmaktadır. Bu amaçla, üniversitelerin aday öğrencilerine yönelik 147 adet web sayfası tespit edilmiş ve çeşitli otomatik test araçları ile değerlendirilmiştir. Ayrıca, değerlendirme sonrası üniversitelerin benzer davranış kalıplarının belirlenmesi amacıyla kümeleme analizi yapılmıştır. Sonuçlar, devlet ya da vakıf fark etmeksizin üniversitelerin büyük çoğunluğunun erişilebilirlik ve kullanım kalitesine daha az dikkat ettiğini göstermiştir. Değerlendirilen web sayfalarının WCAG 2.0 yönergesine göre düşük uyumluluk seviyesine sahip olduğu görüldü. Güvenlik açısından kritik bir sorun tespit edilmemiştir ancak geliştirilmesi gereken belirli noktalar bulunmuştur. Ayrıca bu çalışma, web sayfalarının erişilebilirlik, kullanılabilirlik ve güvenlik ihlallerin çözülebilmesi için geliştiricilere ve yöneticilere bazı değerli öneriler sunmaktadır.
Türkiye'ye yönelik dış turizm talebi açısından ülkelerin kümeleme analizi ile sınıflandırılması
(Osman SAĞDIÇ, 2022) Aydoğdu Ulukan, Ece; Peker, Serhat
Turizm ülkelerin ekonomik gelişimi için en önemli unsurlardan biridir. Ülkelere gelen yabancı turistlerin verilerinin analiz edilmesi bu gelişime katkı sağlaması açısından büyük önem taşısa da uluslararası turizme yönelik Türkiye’de yeteri kadar çalışma bulunmamaktadır. Bu çalışmanın amacı, farklı ülkelerden Türkiye’ye olan dış turizm talebini kümeleme analizi kullanarak incelemek ve Türkiye’ye turist gönderen bu ülkeleri sınıflandırmaktır. Bu bağlamda, ülkelerin gelir düzeyleri, ülkelerden çıkan turist sayıları, çıkan turist sayılarında Türkiye’nin payı ve turistlerin Türkiye’de konaklama süresi gibi faktörler dikkate alınmış ve iki aşamalı kümeleme yöntemi kullanılarak ülkeler gruplandırılmıştır. Elde edilen ülke grupları, kullanılan değişkenler ışığında karakterize edilmiştir. Bu çalışma sonucunda oluşturulan ülke profillerinin, politika yapıcılarının etkin stratejiler geliştirmesinde yardımcı olacağına inanılmaktadır.

Yazar "Peker, Serhat" seçeneğine göre listele

Sayfa Başına Sonuç

Sıralama seçenekleri