WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 2Citation - Scopus: 9Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm(SPRINGER, 2023-10-24) Alshakree, Firas; Akbas, Ayhan; Rahebi, JavadPalm print identification is a biometric technique that relies on the distinctive characteristics of a person’s palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person’s lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray wolf optimization, we achieved to extract and analyze the unique features of a person’s palm print to create a digital representation that can be used for identification purposes with a high degree of accuracy. To this end, two well-known datasets, the Hong Kong Polytechnic University dataset and the Tongji Contactless dataset, were used for testing and evaluation. The recognition rate of the proposed method was compared with other existing methods such as principal component analysis, including local binary pattern and Laplacian of Gaussian-Gabor transform. The results demonstrate that the proposed method outperforms other methods with a recognition rate of 96.72%. These findings show that the combination of deep learning and gray wolf optimization can effectively improve the accuracy of human identification using palm print images.Article A rational utilization of reinforcement material for flexural design of 3D-printed composite beams(SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, 2019) Ciftci, Cihan; Sas, Hatice S.Recent developments in composite industry address the adaptation of 3D printing technology to overcome the design and manufacturing challenges of the traditional composite processing techniques. This adaptation can be performed with the development of design methodologies corresponding to the type of structural load-carrying members in a structure. Considering the frequently use of beams in structures, the development of the design methodology of beams is essential for the adaptation of the additive manufacturing. Therefore, in this paper, the flexural loading concept is analytically formulated to derive moment capacity for the flexural behavior of 3D-printed composite beams. Then, the formulation is adapted to develop a design methodology of 3D-printed laminates under flexural loading. Additionally, the analytical solutions developed for the design methodology presented in this paper were verified with a good agreement with experimental studies.