Yaşar Diner, Öznur

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Yaşar Diner, Öznur
Yaşar Diner,Ö.
Oznur, Yasar Diner
O. Yaşar Diner
Yasar Diner, Oznur
Y., Öznur
Oznur Yaşar Diner
Yasar Diner,Ö.
Yaşar Diner, Ö.
YAŞAR DINER, ÖZNUR
YAŞAR DINER, Öznur
Y.,Oznur
Yasar Diner,O.
Öznur Yaşar Diner
Yaşar Diner, ÖZNUR
Yaşar Diner, O.
Y., Oznur
Ö. Yaşar Diner
ÖZNUR YAŞAR DINER
Yaşar Diner, Oznur
Yasar Diner,Oznur
Öznur YAŞAR DINER
Diner, Öznur Yaşar
Diner, Oznur Yasar
Diner, Ö.Y.
Job Title
Dr. Öğr. Üyesi
Email Address
oznur.yasar@khas.edu.tr
Main Affiliation
Computer Engineering
Status
Website
ORCID ID
0000-0002-9271-2691
Scopus Author ID
58777832500
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAT-7443-2020
Scholarly Output

17

Articles

9

Citation Count

0

Supervised Theses

1

Filters

2015 - 20172
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Author: Diner, Öznur YaşarSubject: Graph theory

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 14
    Strategic Early Warning System for the French Milk Market: a Graph Theoretical Approach To Foresee Volatility
    (Elsevier, 2017) Bisson, Christophe; A. Bısson, Chrıstophe Louıs; Diner, Öznur Yaşar; Yaşar Diner, Öznur
    This paper presents a new approach for developing a Strategic Early Warning System aiming to better detect and interpret weak signals. We chose the milk market as a case study in line with the recent call from the EU Commission for governance tools which help to better address such highly volatile markets. Furthermore on the first of April 2015 the new Common Agricultural Policy ended quotas for milk which led to a milk crisis in the EU. Thus we collaborated with milk experts to get their inputs for a new model to analyse the competitive environment. Consequently we constructed graphs to represent the major factors that affect the milk industry and the relationships between them. We obtained several network measures for this social network such as centrality and density. Some factors appear to have the largest major influence on all the other graph elements while others strongly interact in cliques. Any detected changes in any of these factors will automatically impact the others. Therefore scanning ones competitive environment can allow an organisation to get an early warning to help it avoid an issue (as much as possible) and/or seize an opportunity before its competitors. We conclude that Strategic Early Warning Systems as a corporate foresight approach utilising graph theory can strengthen the governance of markets. (C) 2017 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 0
    Citation - Scopus: 0
    Bayesian and Graph Theory Approaches To Develop Strategic Early Warning Systems for the Milk Market
    (Springer-Verlag Berlin, 2015) Gürpınar, Furkan; A. Bısson, Chrıstophe Louıs; Bisson, Christophe; Yaşar Diner, Öznur; Diner, Öznur Yaşar
    This paper presents frameworks for developing a Strategic Early Warning System allowing the estimatation of the future state of the milk market. Thus this research is in line with the recent call from the EU commission for tools which help to better address such a highly volatile market. We applied different multivariate time series regression and Bayesian networks on a pre-determined map of relations between macro economic indicators. The evaluation of our findings with root mean square error (RMSE) performance score enhances the robustness of the prediction model constructed. Finally we construct a graph to represent the major factors that effect the milk industry and their relationships. We use graph theoretical analysis to give several network measures for this social network