Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Quality function deployment application for improving quality of education in business schools

Abstract: There is an increasing competition between universities globally to attract students. At this point, to compete, it is imperative for the universities to improve the quality of education provided for their stakeholders, including students, parents, and employers. For improving the quality of education, first of all, the universities should make efforts to understand stakeholder expectations, by analyzing their needs and wants. For this purpose, the authors incorporated the quality function deployment technique with fuzzy analytical network process. Due to the vagueness present in the evaluation of relative importance of the criteria for higher education, fuzzy set theory is integrated.

Alternative Media and the Securitization of Climate Change in Turkey

Abstract: Studies on securitization dynamics in a growing number of sectors have been conducted, including securitization of climate change. However, a relatively understudied agent of securitization is medi...

A Two-Factor Authentication System with QR Codes for Web and Mobile Applications

Abstract: The use of QR code-based technologies and applications has become prevalent in recent years where QR codes are accepted to be a practical and intriguing data representation / processing mechanism amongst worldwide users. The aim of this study is to design and implement an alternative two-factor identity authentication system by using QR codes and to make the relevant mechanism and process that could be more user-friendly and practical than one-time password mechanisms used with similar purposes today. The proposed model in this project has been designed in order to enable the verification and validation steps with several security and networking options during the logon process. The model has been implemented by developing a two-factor identity verification system where the second factor is the user's smart / mobile phone device and a pseudo-randomly generated alphanumerical QR code which is used as the one-time password token sent to the user via e-mail or MMS. The proposed model has been developed using C#, asp.net and jQuery languages with symmetrical and asymmetrical cryptography standards for database encryption / hashing and network infrastructure and it has been tested as a prototype where promising results are observed regarding the efficiency, speed and security requirements for today's on-line financial services and similar e-commerce systems.

Data analytics for quality management in Industry 4.0 from a MSME perspective

Abstract: Advances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated data-driven techniques. This study presents a 3-stage model that classifies products depending on defects (defects or non-defects) and defect type according to their levels. This article seeks to detect potential errors to ensure superior quality through machine learning and data mining. The proposed model is tested in a medium enterprise—a kitchenware company in Turkey. Using the main features of data set, product, customer, country, production line, production volume, sample quantity and defect code, a Multilayer Perceptron algorithm for product quality level classification was developed with 96% accuracy. Once a defect is detected, an estimation is made of how many re-works are required. Thus, considering the attributes of product, production line, production volume, sample quantity and product quality level, a Multilayer Perceptron algorithm for re-work quantity prediction model was developed with 98% performance. From the findings, re-work quantity has the highest relation with product quality level where re-work quantities were higher for major defects compared to minor/moderate defects. Finally, this work explores the root causes of defects considering production line and product quality level through association rule mining. The top mined rule achieves a confidence level of 80% where assembly and material were identified as main root causes.