Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/203

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    Article
    Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm
    (SPRINGER, 2024) Alshakree, Firas; Akbas, Ayhan; Rahebi, Javad; 0000-0002-6425-104X; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Akbas, Ayhan; 01. Abdullah Gül University
    Palm 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.
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    Article
    Hydroponic Agriculture with Machine Learning and Deep Learning Methods
    (Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, Mehmet; 0000-0002-1895-8749; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Bulut, Nurten; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
    In 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.
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    Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements (vol 82, pg 68, 2019)
    (ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020) Ayyildiz, Cem; Erdem, H. Emre; Dirikgil, Tamer; Dugenci, Oguz; Kocak, Taskin; Altun, Fatih; Gungor, V. Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; 01. Abdullah Gül University
    This paper presents a system that monitors the health of structural elements in Reinforced Concrete (RC), concrete elements and/or masonry buildings and warn the authorities in case of physical damage formation. Such rapid and reliable detection of impairments enables the development of better risk management strategies to prevent casualties in case of earthquake and floods. Piezoelectric (PZT) sensors with lead zirconate titanate material are the preferred sensor type for fracture detection. The developed sensor mote hardware triggers the PZT sensors and collects the responses they gather from the structural elements. It also sends the collected data to a data center for further processing and analysis in an energy-efficient manner utilizing low-power wireless communication technologies. The access and the analysis of the collected data can be remotely performed via a web interface. Performance results show that the fractures serious enough to cause structural problems can be successfully detected with the developed system.