Yazar "Can, Celal" seçeneğine göre listele

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    Öğe
    A deep convolutional neural network model for hand gesture recognition in 2D near-infrared images
    (Iop Publishing Ltd, 2021) Can, Celal; Kaya, Yasin; Kılıç, Fatih
    The hand gesture recognition (HGR) process is one of the most vital components in human-computer interaction systems. Especially, these systems facilitate hearing-impaired people to communicate with society. This study aims to design a deep learning CNN model that can classify hand gestures effectively from the analysis of near-infrared and colored natural images. This paper proposes a new deep learning model based on CNN to recognize hand gestures improving recognition rate, training, and test time. The proposed approach includes data augmentation to boost training. Furthermore, five popular deep learning models are used for transfer learning, namely VGG16, VGG19, ResNet50, DenseNet121, and InceptionV3 and compared their results. These models are applied to recognize 10 different hand gestures for near-infrared images and 24 ASL hand gestures for colored natural images. The proposed CNN model, VGG16, VGG19, Resnet50, DenseNet121, and InceptionV3 models achieve recognition rates of 99.98%, 100%, 99.99%, 91.63%, 82.42% and 81.84%, respectively on near-infrared images. For colored natural ASL images, the models achieve recognition rates of 99.91%, 99.31%, 98.67%, 91.97%, 93.37%, and 93.21%, respectively. The proposed model achieves promising results spending the least amount of time.
  • [ X ]
    Öğe
    Locating charging stations for electric buses
    (Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, 2025) Can, Celal; Kılıç, Fatih; Kaya, Yasin
    Electric buses have emerged as a crucial component in the transition to low-carbon public transportation systems, owing to their potential to reduce greenhouse gas emissions, mitigate air pollution, and lower urban noise levels. This thesis presents a novel optimization-based spatial planning framework for the deployment of fast-charging stations, designed explicitly for e-bus networks. The primary objective of the proposed model is to facilitate the transition to sustainable public transportation by minimizing the total system cost, which encompasses installation costs, operation and maintenance costs, transportation costs between e-bus routes and charging stations, and infrastructure costs associated with energy distribution lines. To solve the complex optimization problem, this study proposes a binary version of the Walrus Optimization Algorithm (BWAOA), a recently developed nature-inspired metaheuristic technique. The performance of BWAOA is benchmarked against three metaheuristic algorithms, the Arithmetic Optimization Algorithm, the Grey Wolf Optimization, and the Whale Optimization Algorithm, to verify its efficiency and robustness. The proposed model is applied to two real-world case studies based on datasets from Adana, Turkey, which include both public transportation network data and electric power infrastructure data. Experimental results reveal that the BWAOA-based model achieves superior performance in terms of cost minimization, solution stability, and convergence behavior. Additionally, the spatial distribution of selected charging station locations demonstrates improved accessibility for the e-bus network.