Browsing by Author "Fidan, Fatma Şener"

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Çevresel, Sosyal ve Ekonomik Kriterler Dikkate Alınarak MABAC Yöntemi ile En Uygun Konut Kredisi Sağlayıcısının Belirlenmesi
(2023) Fidan, Fatma Şener
Türkiye'de bankacılık sektörü, aktif büyüklüğü ile en önemli sektörlerden biri olup sektörün en büyük aktif kalemi olan kredilerdir. Küresel yasal düzenlemeler ve artan rekabet nedeniyle bankalar mevcut konumlarını koruyabilmek için ekonomik faktörlerin yanı sıra çevresel ve sosyal faktörleri de göz önünde bulundurmak zorundadırlar. Bu nedenle bu çalışmada, bankalar tarafından sağlanan konut kredisinin ekonomik değerlendirmesinin yanı sıra çevresel ve sosyal kriterleri de dikkate alınarak değerlendirilmesi için bir Çok Kriterli Karar Verme (ÇKKV) problemi ortaya konulmuştur. Literatürde elde edilen kriterlerle Türkiye'de yerleşik 7 bankanın bütünleşik değerlendirmesinde Çok Nitelikli Sınır Yakınlaştırma Alanı Karşılaştırması (MABAC) metodu kullanılmıştır. Elde edilen sıralama da Ziraat Bankası birinci, İş Bankası ikinci ve Vakıfbank üçüncü sırada yer almıştır.
Dilsel Özetleme ile Kentsel Hareketlilik Kalıpları: Bisiklet Verilerinden İçgörüler
(2023) Aydoğan, Sena; Akay, Diyar; Fidan, Fatma Şener
This study examined how urban mobility patterns might be analyzed using linguistic summarization, one of the descriptive data analytics tools. The paper focused on urban bicycle-sharing system data, a rich source of knowledge for comprehending urban mobility patterns. The study used a dataset with several variables: day, hour, station, and user type. The data was turned into linguistic descriptions that offer valuable insights into urban mobility through the strength of the fuzzy set. The analysis of travel patterns included identifying busy stations at various times of the day, user segment preferences (students vs. non-students), and changes in general mobility. The results of the linguistic summarization of the urban cycling data allowed for a more thorough knowledge of urban travel patterns. Urban planners, decision-makers, and transportation authorities may optimize the city's current infrastructure, increase accessibility, and meet its residents' wide range of needs thanks to the results that shed light on the dynamics of urban mobility. The study showed how practical descriptive data analytics can be in revealing information, mainly when used to examine travel patterns utilizing information from urban bicycles.
Generating Linguistic Advice for the Carbon Limit Adjustment Mechanism
(Springer Science and Business Media Deutschland GmbH, 2024) Fidan, Fatma Şener; Aydogan, Sena; Akay, Diyar
Linguistic summarization, a subfield of data mining, generates summaries in natural language for comprehending big data. This approach simplifies the incorporation of information into decision-making processes since no specialized knowledge is needed to understand the generated language summaries. The present research employs linguistic summarization to examine the circumstances surrounding the Carbon Border Adjustment Mechanism, one of the most significant regulations confronting exporting nations to the European Union, and will be adopted to support sustainable growth. In this paper, associated with several attributes of the countries and product flow from exporting countries to European countries were defined as nodes and relations, respectively. Before the modeling phase, fuzzy c-means automatically identified fuzzy sets and membership degrees of attributes. During the modeling phase, summary forms were generated using polyadic quantifiers. A total of 1944 linguistic summaries were produced between exporting countries and European countries. Thirty-five summaries have a truth degree greater than or equal to the threshold value of 0.9, which is considered reasonable. The provision of natural language descriptions of the Carbon Border Adjustment Mechanism is intended to aid decision-makers and policymakers in their deliberations. © 2023 Elsevier B.V., All rights reserved.
Tekstil Atıksu Arıtma Tesisinde Nötralizasyon Prosesinin Yaşam Döngüsü Değerlendirmesi
(2020) Uzal, Nıgmet; Aydogan, Emel Kızılkaya; Fidan, Fatma Şener
Although industrial wastewater treatment plants (WWTP) have become an important part of textile facilities in reducing environmental pollution problems, they also produce sludge and various emissions such as high chemical oxygen demand, color and conductivity which have serious negative impacts on the environment. One of the processes with enormous chemical consumption in industrial WWTP of textile facilities is the neutralization process, which aims to adjust the pH of the wastewater. Neutralization processes needed to be optimized in order to determine its overall environmental impacts and then identify the most environmentally appropriate options. The aim of this study is to compare the environmental impacts of carbon dioxide and sulfuric acid, which are two alternative chemicals used in the neutralization process of textile facilities, using Life Cycle Assessment (LCA) approach. The environmental impacts resulting from the use of these two chemicals proposed according to the Reference document on Best Available Techniques (BREF) Document for Textile Industry were revealed by the CML-IA method and the gate-to-gate method. According to the results, using carbon dioxide instead of sulfuric acid, the best improvement was in the abiotic depletion category with 92%, while the least improvement was in the eutrophication potential with 39%. No improvement was observed in the global warming potential and human toxicity impacts.