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.

Surf tourism development: perspectives in alaçati surf spot

Abstract: The main objective of this research is to analyze and evaluate the perceptions and opinions of employees and visitors on sustainable surf tourism in seven surf centers in a popular tourism destination called Alacati, Turkey. Based on the interviews in seven schools by using the semi-structured interview technique, research findings revealed that construction and housing around the surf destination should be limited and natural texture should be preserved in the bay area. Formation of surf camps for kids from various age groups are critical for the development of surf tourism industry. Innovative windsurf related activities should be organized to attract the attention of domestic and international visitors. This exploratory research sheds a light in this field of study for researchers, practitioners and sports tourism professionals.

A framework model for data reliability in wireless sensor networks

Abstract: In this study a model for data reliability in wireless sensor networks is proposed, in which machine learning methods are used. Proposed framework includes data modelling, missing data prediction, anomaly detection, data fusion and trust mechanism phases. Thus, temporal analysis is performed on the preprocessed sensor data and missing data are predicated. Then outliers on collected data are detected on the cluster head nodes by using Eta one-class Support Vector Machines. If an event is detected data are fused and then send to sink. If an anomaly is detected for a node's data, the trust weight of the node is decreased.

The Biometric Signature as a Blockchain Application

Abstract: The Internet would be the largest one had it been a country of the world. The population and the produced and consumed data in this new country have been exponentially increasing, and the metrics with which one can measure all this growth are zettabytes, if not the yottabytes now. The issue of forgery detection on authentication and integrity and on the ownership of the data and the users grows incrementally along with this unprecedented growth of the Internet. With the advancements in hardware and software resulting in the Internet of Things (IoT), the checking against forgery will be even harder to perform since anything and everything will be interconnected as never seen before. Signing a document has always been a direct proof for belonging and authentication of the person. Thus, forgery has always been an equally likely issue to be encountered. While the journey of the signatures in the Internet era will be browsed in this chapter, the biometric signature as a complete and better-suited answer to the problem of forgery will be introduced within ironclad protection provided by a blockchain structure which is a new cybersecurity architecture in the domain of the IoT.

End-To-End Learning from Demonstation for Object Manipulation of Robotis-Op3 Humanoid Robot

Abstract: Humanoid robots are deployed ranging from houses and hotels to healthcare and industry environments to help people. Robots can be easily programed by users to predefined tasks such as walking, grasping, stand-up, and shake-up. However, in these days, all robots are expected to learn itself from the obtained experience by watching the environment and people in there. In this study, it is aimed for Robotis-Op3 humanoid robot to grasp the objects by learning from demonstrations based on vision. A new algorithm is proposed for this purpose. Firstly, the robot is manipulated from user commands and the raw images from the camera of Robotis-Op3 are collected. Secondly, a semantic segmentation algorithm is applied to detect and recognize the objects. A new model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is then proposed to learn the user demonstrations. The results were compared in terms of training time, performance, and model complexity. Simulation results showed that new models produced a high performance for object manipulation.

Logistics systems and optimisation strategies under uncertain operational environment

Abstract: Transportation and logistics systems are characterised by their highly dynamic structures along with numerous interconnected processes. The natures of these systems involve various levels of resource allocation decisions where usually it is not always possible to execute these decisions in the field on time at the best possible way because of the unpredictable factors in plans. By considering the uncertain operational environment, this paper explores the uncertainty issue within operational systems and deals with the problem of allocating resources to maximise expected total profit and minimise inefficiencies under uncertainty. The aim is to design, develop, visualise, and effectively deal with a more realistic model to satisfy uncertain demand nodes by leaving minimal or no unsatisfied zones within an operational environment at seaports, transportation, logistics and supply chain systems. A representative optimisation model, which is developed to address the uncertainty issue, has been solved by using an optimisation algorithm. The results show that operational plans without the utilisation of uncertainty models could have negative impacts, including increased emissions, negative environmental effects, along with higher costs to organisations.