Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Book Part Citation - Scopus: 3People’s Republic of China(SPRINGER, 2022) Alsancak, İbrahim; Aydın, Güldenur; Islam, Md. NazmulThe People’s Republic of China (PRC) was established on October 1, 1949, and it is governed by a single-party system, The Communist Party of China (CCP). The president is also the Chinese Communist Party’s leader. The People’s Republic of China’s (PRC) new Constitution was approved in 1982. The Presidency of the People’s Republic of China, the State Council, the Central Military Commission, the National People’s Congress, the Political Consultative Conference of the People of China, the Supreme Court, and the Attorney General are the central organs of the state. Although PRC does not define itself as a federal state, it has five autonomous regions, territories, counties, rural towns, and it has created some constitutional governance understanding for these local governments. NGOs are recently developing in China. China is also leading country about artificial intelligence and high techs.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.