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                <text>Conference Papers</text>
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              <text>Performance Analysis of Deep Learning Pretrained Image Classifiation Models</text>
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              <text>Convolutional Neural Networks (CNNs); Deep Learning; Image classification</text>
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              <text>Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research.  2023 IEEE.</text>
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              <text>Deepa; Alli A.; Sheetal; Prasad Km S.</text>
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              <text>IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/AIKIIE60097.2023.10390378" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/AIKIIE60097.2023.10390378&lt;/a&gt;
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              <text>ISBN: 979-835031646-9</text>
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              <text>Online</text>
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              <text>Deepa, Christ University, Department of Computer Science, Bengaluru, India; Alli A., Presidency College, Department of Computer Applications, Bengaluru, India; Sheetal, Presidency College, Department of Computer Science and Engineering, Bengaluru, India; Prasad Km S., Rao Bahadur y Mahabaleswarappa Engineering College, Department of Computer Applications, Karnataka, Ballari, India</text>
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