2 results listed
Long Short Term Memory (LSTM) is one of the deepest learning methods capable of
learning along a chain. The method has a chain of modules able to repeating information and
transferring it to the next module. Due to this feature, it is a convenient method for data sets
consisting of time-dependent information such as finance. Bitcoin, using blockchain technology,
has become one of the most popular cryptocurrencies today. Bitcoin data is a time series. In this
study, price estimation model is proposed by using Long-Short Term Memory method for a Bitcoin
price estimation for multivariate time series consisting of opening price, closing price, highest
price, lowest price, Bitcoin volume, Purchasing volüme and weighted price variables. In addition,
the application has been developed in Python programming language.
International Data Science & Engineering Symposium
IDSES
Ali Osman ÇIBIKDİKEN
Ebru Şeyma KARAKOYUN
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