Person: DOĞAN, BUKET
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DOĞAN
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BUKET
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Publication Metadata only Data privacy in big data: Federated learning(Bilgi Kültür Sanat Yayınevi, 2023-06-01) BÜYÜKTANIR, BÜŞRA; DOĞAN, BUKET; Büyüktanır B., Doğan B.With the advancement of technology, the place of internet-based devices in our lives has increased day by day. With these devices, more data has been produced and thus the concept of big data has entered our lives. The big data produced includes various information as well as personal information. The working performance of artificial intelligence technology used in internet-based devices is directly proportional to large and various data. However, at this point, it is of great importance to ensure the privacy of the personal data used. Due to data privacy, in some organizations, data is used where it is produced, but data sharing is not done. This situation both negatively affects the development of artificial intelligence applications and limits the new productions that will emerge by processing the data produced in this field. As a solution to all these problems, federated learning technology has been developed. Federated learning is an up-to-date technology that enables model training without sacrificing data privacy. In this study, the working architectures of the big data concept and federated learning technology are explained, the current studies in the literature are reviewed and their usage areas are summarized. It is thought that this study will contribute to researchers who will work on federated learning for big data, which is up-to-date and open to development.Publication Metadata only Municipal solid waste management: A case study utilizing DES and GIS(2023-10-02) ÇALIŞ USLU, BANU; DOĞAN, BUKET; ÜLKÜ, EYÜP EMRE; Çaliş Uslu B., Kerçek V. A., Şahin E., Perera T., Doğan B., Ülkü E. E.This research aims to compare two well-known solution methodologies, namely Geographical Information Systems (GIS) and Discrete Event Simulation (DES), which are used to design, analyze, and optimize the solid waste management system based on the locations of the garbage bins. A significant finding of the study was that the application of the simulation methodology for a geographical area of a size of 278km2was challenging in that the addition of the geographical conditions to the developed model proved to be time-consuming. On the other hand, the simulation model that was developed without adding geographical conditions revealed that the number of bins could be reduced by 60.3% depending on the population size and garbage density. However, this model could not be implemented since the required walking distance was higher than 75 m, which is greater than the distance the residents could be reasonably expected to travel to reach a bin. Thus, using a cutoff value of 75 m, the total number of bins can be reduced by 30% on average with regard to the result obtained from the GIS-based solution. This can lead to an annual cost reduction of 93.706 € on average in the collection process and carbon dioxide release reduction of 18% on average.Publication Metadata only Data privacy in big data: Federated learning(2023-06-25) BÜYÜKTANIR, BÜŞRA; DOĞAN, BUKET; Büyüktanır B., Doğan B.