Zeynep Didem UNUTMAZ DURMUŞOĞLU

Dr. Zeynep Didem UNUTMAZ DURMUŞOĞLU


Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2013 in Expert Systems with Applications
Verified
Journal Article
Closed access
ABAdil Baykasoglu
Adil Baykasoglu
VKVahit Kaplanoğlu
Vahit Kaplanoğlu
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU
CSCenk Sahin
Cenk Sahin

1 August 2018 in Technology in Society
Verified
Journal Article
Closed access
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU

1 May 2008 in European Journal of Operational Research
Verified
Journal Article
Closed access
ABAdil Baykasoglu
Adil Baykasoglu
MGMustafa Göçken
Mustafa Göçken
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU

1 February 2011 in Computers in Industry
Verified
Journal Article
Closed access
ABAdil Baykasoglu
Adil Baykasoglu
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU
VKVahit Kaplanoglu
Vahit Kaplanoglu

1 January 2014 in International Journal of Computational Intelligence Systems
Verified
Journal Article
Open access
PDF file
ABAdil Baykasoglu
Adil Baykasoglu
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU

14 July 2017 in Human and Ecological Risk Assessment: An International Journal
Verified
Journal Article
Closed access
ZDZeynep Didem UNUTMAZ DURMUŞOĞLU
Zeynep Didem UNUTMAZ DURMUŞOĞLU
This study proposes a method to assess the public understanding toward themes related to environment, biodiversity conservation and fishery. Since the Internet traffic is seen as the proxy of the public interest, this article analyzes the related Google Trends data to explore the cognitive map of the public. Patterning changes in the search volume (synchronous increase and decrease) were detected by using the association analysis. Weekly search volumes of the United States for the last 5 years have been gathered and were converted into values ranging between –1 and 1, indicating the gradual level of increase/decrease by the time. Apriori algorithm was used to determine the associations between search volumes of the keywords. As a consequence, several patterns were detected with a significant support indicating synchronous increases/decreases of the volumes. Upon the development of cognitive maps based on the detected patterns, it is found that the terms “Environment” and “Pollution” were the most significant and central terms. The search volume of the terms such as “Conservation,” “Ecology,” “Climate change,” “Invasive species” and “Endangered Species” increases as the search increases in the central terms. Therefore, these terms can be seen as the drivers/triggers of the Google search for some central words. It is apparent that the public has awareness of the central terms and further interest should be provoked and fostered on the environment-related concepts.