Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi

Kaya, Volkan; Baran, Ahmet; Tuncer, Servet

doi:10.31590/ejosat.845467

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…This approach is that instead of the layers learning from the underlying mapping, the mesh now allows the mesh to conform to the mapping. This allows to train much deeper neural networks (Kaya, 2021). The residual learning block structure that makes up the network is given in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…Then, 𝑥𝑥 is added to the 𝐹𝐹(𝑥𝑥) function to obtain the 𝐻𝐻(𝑥𝑥) function. This situation is expressed as 𝐻𝐻(𝑥𝑥) = 𝐹𝐹(𝑥𝑥) + 𝑥𝑥 (He et al, 2016;Kaya et al, 2020). In the classical network model, 𝐻𝐻(𝑥𝑥) is equal to the 𝐹𝐹(𝑥𝑥) function, while the original data is added to the input in the ResNet model (Toğaçar and Ergen, 2019).…”

Section: Methodsmentioning

confidence: 99%

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Classification Of Pistachio Images With The ResNet Deep Learning Model

Avuçlu

2023

SJAFS

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Pistachio, which is grown in many parts of the world today, has an important place in the agricultural economy. In order to maintain this economic value, the post-harvest industrial classification process is very important to obtain efficiency from this harvest. In the process of separating pistachios, an efficient classification process is needed in order for different pistachio species to appeal to different markets. For this reason, the classification process of pistachios is very important. In this study, Kirmizi and Siirt pistachio classification with 2148 images was made using ResNet architecture. After the statistical experimental studies, the highest classification accuracy was obtained from fold-1 as 88.5781% and the Accuracy value was 0.86168 after the classification process.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Classification Of Pistachio Images With The ResNet Deep Learning Model

Avuçlu

2023

SJAFS

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“…VGGNet is a standard convolutional neural network architecture with multi-layer GPU support (Kaya et al, 2020). VGGNet architecture forms the basis of object recognition models.…”

Section: Figure 2 Vggnet Model Diagrammentioning

confidence: 99%

Classification of skin cancer using VGGNet model structures

Kaya

Akgül

2022

Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi

Self Cite

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Skin cancer is one of the most common type of cancer in humans. This type of cancer is produced by skin cells called melanocytes and occurs as a result of division and multiplication of the mentioned cells. The most important symptom of skin cancer is the formation of spots on the skin or the observation of changes in the shape, color, or size of the existing spot. It is necessary to consult a specialist to distinguish the difference between a normal spot and skin cancer. Expert physicians examine and follow up the spots on the skin using skin surface microscopy, called dermatoscopy, or take a sample from the suspicious area and request it to be examined in laboratory environment. This situation increases the cost of the procedure for the diagnosis of skin cancer and also causes it to be treated at a later stage. Therefore, there is a need for a metod that can detect skin cancer early. Thanks to machine learning, become popular in recent years, many diseases can be diagnosed with software that helps expert physicians. In this study, VGGNet model structures (VGG-11, VGG-13, VGG-16, VGG-19) that quickly classify skin cancer and become a traditional convolutional neural network architecture using deep learning method, a subfield of machine learning, were used. It has been observed that the VGG-11 architecture, which is one of the VGGNet model structures, detects skin cancer with superior success accuracy (83%) compared to other model structures.

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“…The deterioration of the railway line, the wear of the materials on the lines over time, foreign materials falling on the railway, and deliberate sabotage on the railway put the safety of railway passengers at risk. Considering all these factors, a fast and efficient inspection system is required to ensure the safety of railways [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

EYLENCE

YÜCEL>

Özmen

et al. 2022

Türk Doğa Ve Fen Dergisi

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With the developing technology, technological blessings make human life easier and help them every day. Unmanned aerial vehicles (UAV), which is one of the technological blessings, have shown themselves in many fields, especially in fields such as the military, defense industry, photography, and hobby. With the development of defense systems with UAVs, the security of railways has also been left to UAVs. In this study, while the foreign matter separation is made on the railway by using the deep learning model in real-time, the image taken on the UAV is simultaneously controlled by using the image processing method. The fact that the deep learning model has a 0.99 mAP rate increases the reliability of the model.

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Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi

Cited by 3 publications

References 19 publications

Classification Of Pistachio Images With The ResNet Deep Learning Model

Classification Of Pistachio Images With The ResNet Deep Learning Model

Classification of skin cancer using VGGNet model structures

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

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