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Bulent Sezen

Gebze Technical University, Business Administration, Faculty Member

Gebze Institute Of Technology, Business Administrations - Operations and Management, Faculty Member

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Bülent Sezen is a Prof. in Operations Management at Gebze Technical University (Kocaeli-Türkiye). His interest areas include computer vision, automation, machine learning and optimization applied in manufacturing and service industries and in the context of operations and supply chain management. His research studies have been published in many scholarly journals.

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Gebze Technical University

Mihaela Matešić

Eastern Mediterranean University

Kyandoghere KYAMAKYA

Adebayo Abayomi-Alli

Linnaeus University

Centre National de la Recherche Scientifique / French National Centre for Scientific Research

Aasif Ahmad Mir

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Papers by Bulent Sezen

Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer

Electronics

The purpose of this study was to prove the use of content and sentiment analysis to understand pu... more The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions o...

An Exploratory Content and Sentiment Analysis of The Guardian Metaverse Articles Using Leximancer and Natural Language Processing

The metaverse has become one of the most popular concepts of recent times. Companies and entrepre... more The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021–2022 published on The Guardi...

Yapay Sinir Ağları ile Trafik Yoğunluğu Tahmini

Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi

Şehirlerin büyük problemlerden olan trafik yoğunluğu, insan hayatını birçok yönden etkilemektedir... more Şehirlerin büyük problemlerden olan trafik yoğunluğu, insan hayatını birçok yönden etkilemektedir. Daha yaşanabilir bir kent hayatı için; mevcut yoğunluğun belirlenerek araç trafiğinin kontrol edilebilmesi ve ileride yaşanabilecek sıkışıklıklar için gerekli önlemlerin alınabilmesi gerekmektedir. Trafik yoğunluğunu arttırabilecek muhtemel parametreler dikkate alınarak gerçekleşecek bir yoğunluk tahminin, kent sakinleri açısından olduğu kadar sürücüler ve bir şehrin mümkün olan en az trafik yoğunluğuna maruz kalmasından sorumlu olan yetkili kurum ve kuruluşlar için önemli bir yeri vardır. Çalışma, İstanbul’ a Tuzla ilçesinden giriş yönünde, E-5 karayolu üzerinde yapılmıştır. Veriler, Yandex Trafik ve Wunderground internet sitelerinden elde edilmiştir. Hafta içi günler, mesai saatleri, havanın genel durumu, sıcaklık, rüzgârın hızı ve nem seviyeleri bağımsız değişken olarak alınmıştır. Gün, saat ve hava koşulu değişkenleri kategoriktir ve bu değişkenler kukla değişken olarak belirlenmiş...

Defining Virtual Consumerism Through Content and Sentiment Analyses

by Sezai Tunca and Bulent Sezen

Cyberpsychology, Behavior, and Social Networking

Solar Cell Busbars Surface Defect Detection Based on Deep Convolutional Neural Network

IEEE Latin America Transactions

Defect detection of the solar cell surface with texture and complicated background is a challenge... more Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognition reaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.

The role of communication quality, relational commitment, and reciprocity in building supply chain resilience: A social exchange theory perspective

Transportation Research Part E: Logistics and Transportation Review

Qualitative Content Analysis of The New York Times Coronavirus (2019-n-COV) Articles with Natural Language Processing (NLP) and Leximancer

This study aimed to analyze coronavirus (2019-nCOV) discourse related articles published on NyTim... more This study aimed to analyze coronavirus (2019-nCOV) discourse related articles published on NyTimes.com with NLP methods and Leximancer. The textual data of the research were collected with the Orange3 software text mining tool via the NyTimes.com API, and qualitative content analysis was carried out with Leximancer and NLP methods. The research data were selected as three terms (first-mid-last) depending on the determined date ranges during pandemic. The textual data set was analyzed with Leximancer using the qualitative content analysis method and conducted sentiment measurements of the published articles. Then, using Leximancer concept maps, explained concepts and their relationships by visualizing them to identify pandemic discourse. Leximancer results, the distance and proximity positions of some themes related to NyTimes.com pandemic discourse were revealed according to their conceptual definitions. In context of findings can be concluded that NyTimes.com coronavirus (2019-nCO...

Defining Virtual Consumerism Through Content and Sentiment Analyses

Cyberpsychology, Behavior, and Social Networking

Solar Cell Busbars Surface Defect Detection based on Deep Convolutional Neural Network

by Yavuz S E L I M Balcioglu, Bulent Sezen, and Ceren Çubukçu Çerasi

IEEE LATIN AMERICA TRANSACTIONS, 2023

Defect detection of the solar cell surface with texture and complicated background is a challenge... more Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognition reaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.

Estimation of the Probability of Bank Customers by Artificial Neural Networks

Universal Journal of Management, 2020

One of the most important issues in today's banking sector is that they want to add new customers... more One of the most important issues in today's banking sector is that they want to add new customers to their bodies and want to keep their current customers. For this, banks spend a lot of money. Because of the methods they use, they either have to keep information flow to all their customers or they have to focus on their customers, whose traditional methods of probability leave. In this article, the probability of bank customers left by artificial neural networks is estimated. Presently, with the improvement of technology, a growing number of banks, holding existing clients for banks and combining new clients into their systems have earned significance. As the Bank's efficiency, it is essential to define the clients with the contingency of dropping within existing clients. The client pool generated by the classical methods utilized leads to the introduction of activities on a major number of groups for the bank. This outcomes in higher expenses for banks. The main purpose of this paper is to lay the foundation for further research for precision of who will the bank as a customer. The findings of our study with artificial neural networks have described a minimal and more compressed group as clients who are likely to leave. In this way, it is foreseen that the likely costs of banks will minimize. As a result of this study, the most accurate estimation was obtained by educating artificial neural networks with the most accurate values.

A Proposed Conceptual Model of Destructive Deviance: The Mediator Role of Moral Disengagement

Procedia - Social and Behavioral Sciences, 2015

Effectiveness and efficiency have always been one of the main concerns for organizations. These c... more Effectiveness and efficiency have always been one of the main concerns for organizations. These concepts are commonly used for processes, products, machineries, etc. However, it is obvious that these concepts are also related with the employees and their behaviors. Given the organizations' profit maximization policies, the employee related efficiency losses has been an important concern. At this point, because of its harmful effects, the presence of destructive deviant workplace behaviors is a pivotal factor for organizations. Destructive deviant workplace behaviors are one of the most striking negative behaviors, which threaten the wellbeing of organization as a whole. This paper provides a proposed model on some rarely studied predictors (i.e. careerism, relativism, Machiavellianism and moral disengagement) of destructive deviant workplace behaviors, where moral disengagement plays a mediator role. Managerial and further research implications are also discussed.

Avrupa Birliği’ne Üye Ülkelerdeki ve Türkiye’deki İş Kazaları ile İnsani Gelişme Endeksleri arasında Mukayeseli Bir Çalışma

ABSTRACT

The inhibitors of risk information sharing in the supply chain: A multiple case study in Turkey

Journal of Contingencies and Crisis Management, 2020

One of the greatest threats faced by organizations is disruption in the supply chain arising from... more One of the greatest threats faced by organizations is disruption in the supply chain arising from not sharing risk information among the supply chain partners. The aim of this study is to identify the inhibitors of risk information sharing in a supply chain by using practical‐side evidence. An exploratory multiple case design was utilized to investigate why supply chain partners in Turkey may be reluctant to share risk information among their members and provide solutions to overcome these barriers. The results of the study indicate that the inhibitors of risk information sharing fall into three categories: risk‐related, organization‐related, and management‐related. Furthermore, the findings demonstrate possible solutions for reluctance in risk information sharing among supply chain partners based on managers' experiences.

Image Processing with Deep Learning: Surface Defect Detection of Metal Gears through Deep Learning

by Sezai Tunca and Bulent Sezen

Materials Evaluation

Intelligent production requires improved data analytics and better technological possibilities to... more Intelligent production requires improved data analytics and better technological possibilities to improve system performance and decision making. With the widespread use of the machine learning process, a growing need has arisen for processing extensive production data, equipped with high volumes, high speed, and high diversity. At this point, deep learning provides advanced analysis tools for processing and analyzing extensive production data. The deep convolutional neural network (DCNN) displays state-of-the-art performance on many grounds, including metal manufacturing surface defect detection. However, there is still space for improving the defect detection performance over generic DCNN models. The proposed approach performed better than the associated methods in the particular area of surface crack detection. The defect zones of disjointed results are classified into their unique classes by a DCNN. The experimental outcomes prove that this method meets the durability and effici...

Image Processing with Deep Learning: Surface Defect Detection of Metal Gears through Deep Learning

Materials Evaluation

Intelligent production requires improved data analytics and better technological possibilities to... more Intelligent production requires improved data analytics and better technological possibilities to improve system performance and decision making. With the widespread use of the machine learning process, a growing need has arisen for processing extensive production data, equipped with high volumes, high speed, and high diversity. At this point, deep learning provides advanced analysis tools for processing and analyzing extensive production data. The deep convolutional neural network (DCNN) displays state-of-the-art performance on many grounds, including metal manufacturing surface defect detection. However, there is still space for improving the defect detection performance over generic DCNN models. The proposed approach performed better than the associated methods in the particular area of surface crack detection. The defect zones of disjointed results are classified into their unique classes by a DCNN. The experimental outcomes prove that this method meets the durability and effici...

The Combined Effects of Trust and Employee Identification on Intention to Quit

Bu calisma, yoneticiye (ust amire) guven, orgutsel kimlik ve isten ayrilma niyeti arasindaki ilis... more Bu calisma, yoneticiye (ust amire) guven, orgutsel kimlik ve isten ayrilma niyeti arasindaki iliskileri incelemektedir. Calismada kullanilan veriler, 35 firmada calismakta olan 133 orta seviye yoneticiye sorulan anket sorulari ile elde edilmis olup, bu verilerle, soz konusu degiskenler arasindaki iliskiler uzerine kurulan hipotezler test edilmistir. Anketi olusturan sorularin tamami gecmiste kullanilmis guvenilir anket olceklerinden alinmistir. Anket sorularina verilen cevaplarin ortalama degerleri alinarak olusturulan (bilesik) degiskenler regresyon analizlerine tabii tutuldugunda, yoneticiye guven ve orgutsel kimlik bagimsiz degisken olarak alindiginda, orgutsel kimligin degil yoneticiye guvenin daha cok isten ayrilma niyeti ile iliskili oldugu bulunmustur

Dağıtım Kanallarında Üretici - Bayi İlişkilerinde Bağımlılık Kavramı ve Bağlılığın İşbirliği, Bağımlılık ve Memnuniyet Üzerine Etkileri

Doğuş Üniversitesi Dergisi

Bu makalede dağıtım kanalı içerisinde yer alan üretici firmalar ile aracıları arasındaki karşılık... more Bu makalede dağıtım kanalı içerisinde yer alan üretici firmalar ile aracıları arasındaki karşılıklı iş ilişkileri sonucunda ortaya çıkan bağımlılık kavramının önemi üzerinde durulmaktadır. Bağımlılık kavramının unsurları (özgün yatırımlar, yerine konulabilirlik ve önem) ve bunların üretici-bayi ilişkilerine olan etkisi tartışılmaktadır. Ayrıca bağımlılığın önemli çıktıları olan üç değişken (bağlılık, işbirliği davranışı ve memnuniyet) ile arasındaki bağlantı ayrıntılı olarak incelenmektedir. Makalenin teorik bölümünde ortaya atılan hipotezler, Türkiye'de faaliyet gösteren 192 otomobil bayisine yapılan anket çalışması ile test edilmekte ve sonuçların tartışılması ile makale sona ermektedir.

An Alternative Human Development Index Considering Unemployment

South East European Journal of Economics and Business, 2011

An Alternative Human Development Index Considering UnemploymentThe Human Development Index (HDI) ... more An Alternative Human Development Index Considering UnemploymentThe Human Development Index (HDI) has played an influential role in the debate on human development (HD) for many years. However, no index is perfect and neither is the HDI of the United Nations Development Program (UNDP). This paper aims to construct a new composite index for the development performance of a sample of 30 Organization for Economic Co-operation and Development (OECD) countries by adding a fourth indicator, namely the unemployment index, to the calculation of HDI. The addition of the unemployment factor to the HDI as a new indicator has the potential to make the index more comprehensive and present a suitable approach for assessing the development performance of countries.

Recomputation of Undp’s Hdi Rankings by Data Envelopment Analysis

EMAJ: Emerging Markets Journal, 2011

The HDI has played an influential role in the debate on human development. No index is perfect an... more The HDI has played an influential role in the debate on human development. No index is perfect and so is the Human Development Index of United Nations Development Program. This paper aims to measure the performance of 182 countries in terms of performance by means of non-parametric input oriented CRS employed Data Envelopment Analysis. In addition, it elaborates on the cut-off values assigned by UNDP to categorize the countries. By means of this research, countries will be able to choose those elements by benchmarking from other countries that are applicable and most likely to develop strategy formulation processes for human development and international growth.

The effects of participation in decision making, individual improvement efforts and training on the quality of the product design process

Production Planning & Control, 2007

The aim of this study is to investigate the effects of decision making participation, improvement... more The aim of this study is to investigate the effects of decision making participation, improvement effort and training on quality of product design process. An employee&#x27;s perceived degree of his/her own participation in organisational decision making has been considered a major factor influencing the employee&#x27;s improvement efforts. Improvement effort and training are hypothesised to influence quality of product design process. Hypothesised relationships are tested through a survey study of 90 employees in six ...

Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer

Electronics

The purpose of this study was to prove the use of content and sentiment analysis to understand pu... more The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions o...

An Exploratory Content and Sentiment Analysis of The Guardian Metaverse Articles Using Leximancer and Natural Language Processing

The metaverse has become one of the most popular concepts of recent times. Companies and entrepre... more The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021–2022 published on The Guardi...

Yapay Sinir Ağları ile Trafik Yoğunluğu Tahmini

Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi

Şehirlerin büyük problemlerden olan trafik yoğunluğu, insan hayatını birçok yönden etkilemektedir... more Şehirlerin büyük problemlerden olan trafik yoğunluğu, insan hayatını birçok yönden etkilemektedir. Daha yaşanabilir bir kent hayatı için; mevcut yoğunluğun belirlenerek araç trafiğinin kontrol edilebilmesi ve ileride yaşanabilecek sıkışıklıklar için gerekli önlemlerin alınabilmesi gerekmektedir. Trafik yoğunluğunu arttırabilecek muhtemel parametreler dikkate alınarak gerçekleşecek bir yoğunluk tahminin, kent sakinleri açısından olduğu kadar sürücüler ve bir şehrin mümkün olan en az trafik yoğunluğuna maruz kalmasından sorumlu olan yetkili kurum ve kuruluşlar için önemli bir yeri vardır. Çalışma, İstanbul’ a Tuzla ilçesinden giriş yönünde, E-5 karayolu üzerinde yapılmıştır. Veriler, Yandex Trafik ve Wunderground internet sitelerinden elde edilmiştir. Hafta içi günler, mesai saatleri, havanın genel durumu, sıcaklık, rüzgârın hızı ve nem seviyeleri bağımsız değişken olarak alınmıştır. Gün, saat ve hava koşulu değişkenleri kategoriktir ve bu değişkenler kukla değişken olarak belirlenmiş...

Defining Virtual Consumerism Through Content and Sentiment Analyses

by Sezai Tunca and Bulent Sezen

Cyberpsychology, Behavior, and Social Networking

Solar Cell Busbars Surface Defect Detection Based on Deep Convolutional Neural Network

IEEE Latin America Transactions

Defect detection of the solar cell surface with texture and complicated background is a challenge... more Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognition reaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.

The role of communication quality, relational commitment, and reciprocity in building supply chain resilience: A social exchange theory perspective

Transportation Research Part E: Logistics and Transportation Review

Qualitative Content Analysis of The New York Times Coronavirus (2019-n-COV) Articles with Natural Language Processing (NLP) and Leximancer

This study aimed to analyze coronavirus (2019-nCOV) discourse related articles published on NyTim... more This study aimed to analyze coronavirus (2019-nCOV) discourse related articles published on NyTimes.com with NLP methods and Leximancer. The textual data of the research were collected with the Orange3 software text mining tool via the NyTimes.com API, and qualitative content analysis was carried out with Leximancer and NLP methods. The research data were selected as three terms (first-mid-last) depending on the determined date ranges during pandemic. The textual data set was analyzed with Leximancer using the qualitative content analysis method and conducted sentiment measurements of the published articles. Then, using Leximancer concept maps, explained concepts and their relationships by visualizing them to identify pandemic discourse. Leximancer results, the distance and proximity positions of some themes related to NyTimes.com pandemic discourse were revealed according to their conceptual definitions. In context of findings can be concluded that NyTimes.com coronavirus (2019-nCO...

Defining Virtual Consumerism Through Content and Sentiment Analyses

Cyberpsychology, Behavior, and Social Networking

Solar Cell Busbars Surface Defect Detection based on Deep Convolutional Neural Network

by Yavuz S E L I M Balcioglu, Bulent Sezen, and Ceren Çubukçu Çerasi

IEEE LATIN AMERICA TRANSACTIONS, 2023

Defect detection of the solar cell surface with texture and complicated background is a challenge... more Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognition reaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.

Estimation of the Probability of Bank Customers by Artificial Neural Networks

Universal Journal of Management, 2020

One of the most important issues in today's banking sector is that they want to add new customers... more One of the most important issues in today's banking sector is that they want to add new customers to their bodies and want to keep their current customers. For this, banks spend a lot of money. Because of the methods they use, they either have to keep information flow to all their customers or they have to focus on their customers, whose traditional methods of probability leave. In this article, the probability of bank customers left by artificial neural networks is estimated. Presently, with the improvement of technology, a growing number of banks, holding existing clients for banks and combining new clients into their systems have earned significance. As the Bank's efficiency, it is essential to define the clients with the contingency of dropping within existing clients. The client pool generated by the classical methods utilized leads to the introduction of activities on a major number of groups for the bank. This outcomes in higher expenses for banks. The main purpose of this paper is to lay the foundation for further research for precision of who will the bank as a customer. The findings of our study with artificial neural networks have described a minimal and more compressed group as clients who are likely to leave. In this way, it is foreseen that the likely costs of banks will minimize. As a result of this study, the most accurate estimation was obtained by educating artificial neural networks with the most accurate values.

A Proposed Conceptual Model of Destructive Deviance: The Mediator Role of Moral Disengagement

Procedia - Social and Behavioral Sciences, 2015

Effectiveness and efficiency have always been one of the main concerns for organizations. These c... more Effectiveness and efficiency have always been one of the main concerns for organizations. These concepts are commonly used for processes, products, machineries, etc. However, it is obvious that these concepts are also related with the employees and their behaviors. Given the organizations' profit maximization policies, the employee related efficiency losses has been an important concern. At this point, because of its harmful effects, the presence of destructive deviant workplace behaviors is a pivotal factor for organizations. Destructive deviant workplace behaviors are one of the most striking negative behaviors, which threaten the wellbeing of organization as a whole. This paper provides a proposed model on some rarely studied predictors (i.e. careerism, relativism, Machiavellianism and moral disengagement) of destructive deviant workplace behaviors, where moral disengagement plays a mediator role. Managerial and further research implications are also discussed.

Avrupa Birliği’ne Üye Ülkelerdeki ve Türkiye’deki İş Kazaları ile İnsani Gelişme Endeksleri arasında Mukayeseli Bir Çalışma

ABSTRACT

The inhibitors of risk information sharing in the supply chain: A multiple case study in Turkey

Journal of Contingencies and Crisis Management, 2020

One of the greatest threats faced by organizations is disruption in the supply chain arising from... more One of the greatest threats faced by organizations is disruption in the supply chain arising from not sharing risk information among the supply chain partners. The aim of this study is to identify the inhibitors of risk information sharing in a supply chain by using practical‐side evidence. An exploratory multiple case design was utilized to investigate why supply chain partners in Turkey may be reluctant to share risk information among their members and provide solutions to overcome these barriers. The results of the study indicate that the inhibitors of risk information sharing fall into three categories: risk‐related, organization‐related, and management‐related. Furthermore, the findings demonstrate possible solutions for reluctance in risk information sharing among supply chain partners based on managers' experiences.

Image Processing with Deep Learning: Surface Defect Detection of Metal Gears through Deep Learning

by Sezai Tunca and Bulent Sezen

Materials Evaluation

Intelligent production requires improved data analytics and better technological possibilities to... more Intelligent production requires improved data analytics and better technological possibilities to improve system performance and decision making. With the widespread use of the machine learning process, a growing need has arisen for processing extensive production data, equipped with high volumes, high speed, and high diversity. At this point, deep learning provides advanced analysis tools for processing and analyzing extensive production data. The deep convolutional neural network (DCNN) displays state-of-the-art performance on many grounds, including metal manufacturing surface defect detection. However, there is still space for improving the defect detection performance over generic DCNN models. The proposed approach performed better than the associated methods in the particular area of surface crack detection. The defect zones of disjointed results are classified into their unique classes by a DCNN. The experimental outcomes prove that this method meets the durability and effici...

Image Processing with Deep Learning: Surface Defect Detection of Metal Gears through Deep Learning

Materials Evaluation

Intelligent production requires improved data analytics and better technological possibilities to... more Intelligent production requires improved data analytics and better technological possibilities to improve system performance and decision making. With the widespread use of the machine learning process, a growing need has arisen for processing extensive production data, equipped with high volumes, high speed, and high diversity. At this point, deep learning provides advanced analysis tools for processing and analyzing extensive production data. The deep convolutional neural network (DCNN) displays state-of-the-art performance on many grounds, including metal manufacturing surface defect detection. However, there is still space for improving the defect detection performance over generic DCNN models. The proposed approach performed better than the associated methods in the particular area of surface crack detection. The defect zones of disjointed results are classified into their unique classes by a DCNN. The experimental outcomes prove that this method meets the durability and effici...

The Combined Effects of Trust and Employee Identification on Intention to Quit

Bu calisma, yoneticiye (ust amire) guven, orgutsel kimlik ve isten ayrilma niyeti arasindaki ilis... more Bu calisma, yoneticiye (ust amire) guven, orgutsel kimlik ve isten ayrilma niyeti arasindaki iliskileri incelemektedir. Calismada kullanilan veriler, 35 firmada calismakta olan 133 orta seviye yoneticiye sorulan anket sorulari ile elde edilmis olup, bu verilerle, soz konusu degiskenler arasindaki iliskiler uzerine kurulan hipotezler test edilmistir. Anketi olusturan sorularin tamami gecmiste kullanilmis guvenilir anket olceklerinden alinmistir. Anket sorularina verilen cevaplarin ortalama degerleri alinarak olusturulan (bilesik) degiskenler regresyon analizlerine tabii tutuldugunda, yoneticiye guven ve orgutsel kimlik bagimsiz degisken olarak alindiginda, orgutsel kimligin degil yoneticiye guvenin daha cok isten ayrilma niyeti ile iliskili oldugu bulunmustur

Dağıtım Kanallarında Üretici - Bayi İlişkilerinde Bağımlılık Kavramı ve Bağlılığın İşbirliği, Bağımlılık ve Memnuniyet Üzerine Etkileri

Doğuş Üniversitesi Dergisi

Bu makalede dağıtım kanalı içerisinde yer alan üretici firmalar ile aracıları arasındaki karşılık... more Bu makalede dağıtım kanalı içerisinde yer alan üretici firmalar ile aracıları arasındaki karşılıklı iş ilişkileri sonucunda ortaya çıkan bağımlılık kavramının önemi üzerinde durulmaktadır. Bağımlılık kavramının unsurları (özgün yatırımlar, yerine konulabilirlik ve önem) ve bunların üretici-bayi ilişkilerine olan etkisi tartışılmaktadır. Ayrıca bağımlılığın önemli çıktıları olan üç değişken (bağlılık, işbirliği davranışı ve memnuniyet) ile arasındaki bağlantı ayrıntılı olarak incelenmektedir. Makalenin teorik bölümünde ortaya atılan hipotezler, Türkiye'de faaliyet gösteren 192 otomobil bayisine yapılan anket çalışması ile test edilmekte ve sonuçların tartışılması ile makale sona ermektedir.

An Alternative Human Development Index Considering Unemployment

South East European Journal of Economics and Business, 2011

An Alternative Human Development Index Considering UnemploymentThe Human Development Index (HDI) ... more An Alternative Human Development Index Considering UnemploymentThe Human Development Index (HDI) has played an influential role in the debate on human development (HD) for many years. However, no index is perfect and neither is the HDI of the United Nations Development Program (UNDP). This paper aims to construct a new composite index for the development performance of a sample of 30 Organization for Economic Co-operation and Development (OECD) countries by adding a fourth indicator, namely the unemployment index, to the calculation of HDI. The addition of the unemployment factor to the HDI as a new indicator has the potential to make the index more comprehensive and present a suitable approach for assessing the development performance of countries.

Recomputation of Undp’s Hdi Rankings by Data Envelopment Analysis

EMAJ: Emerging Markets Journal, 2011

The HDI has played an influential role in the debate on human development. No index is perfect an... more The HDI has played an influential role in the debate on human development. No index is perfect and so is the Human Development Index of United Nations Development Program. This paper aims to measure the performance of 182 countries in terms of performance by means of non-parametric input oriented CRS employed Data Envelopment Analysis. In addition, it elaborates on the cut-off values assigned by UNDP to categorize the countries. By means of this research, countries will be able to choose those elements by benchmarking from other countries that are applicable and most likely to develop strategy formulation processes for human development and international growth.

The effects of participation in decision making, individual improvement efforts and training on the quality of the product design process

Production Planning & Control, 2007

The aim of this study is to investigate the effects of decision making participation, improvement... more The aim of this study is to investigate the effects of decision making participation, improvement effort and training on quality of product design process. An employee&#x27;s perceived degree of his/her own participation in organisational decision making has been considered a major factor influencing the employee&#x27;s improvement efforts. Improvement effort and training are hypothesised to influence quality of product design process. Hypothesised relationships are tested through a survey study of 90 employees in six ...

An outlook for physical distribution system in durable goods retail: A comparative case study

by A.Zafer ACAR and Bulent Sezen