TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article Hydroponic Agriculture with Machine Learning and Deep Learning Methods(Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, MehmetIn the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.Article Optimizing Parameters for Efficient Computation With Fully Homomorphic Encryption Schemes(Tubitak Scientific & Technological Research Council Turkey, 2025-03-21) Karaagac, Cavidan Yakupoglu; Rohloff, Kurt; Yakupoğlu Karaağaç, Cavidan; Yakupoglu, CavidanIn this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective optimization algorithm to determine the optimal parameter set for performance, CEF, and security simultaneously. As a result of this optimization, we obtain an improved parameter set that enhances performance at a given security level while ensuring correctness and resistance to lattice-based attacks, maintaining at least 128-bit security. Our results achieve an average similar to 5x reduction in CEF and generally better performance compared to the parameter sets in a previous BFVrns study. Our approach serves as a semi-automated parameter selection method for the PALISADE homomorphic encryption library, a widely recognized FHE library. This study sets a precedent for other FHE libraries.