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With the increase in computational power and big data, studies on artificial
intelligence are increasing day by day. Especially deep learning applications are seen in almost
all areas of our lives. The most successful results of deep learning architectures are in image
processing. Different architectural approaches are tried to make image processing fast. Due to
the fact that video images consist of large capacity data, it is very important to achieve high
performance in these video images. In this study, size reduction function has been proposed
that can reduce the size of the high-quality and large-capacity file data and produce results with
a high accuracy rate. The results of the proposed method were compared in terms of
performance and speed with different architectures in image processing using CNN
(Convolutional Neural Network) algorithm. In addition, an application that uses the
recommended size reduction function has also been developed using the Python programming
language.
International Data Science & Engineering Symposium
IDSES
Ömer PİŞGİN
Ali Osman ÇIBIKDİKEN