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All Journal International Journal of Electrical and Computer Engineering
Beldeubayeva, Zhanar
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Computer Science & IT Electrical & Electronics Engineering

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Image noise reduction by deep learning methods Uzakkyzy, Nurgul; Ismailova, Aisulu; Ayazbaev, Talgatbek; Beldeubayeva, Zhanar; Kodanova, Shynar; Utenova, Balbupe; Satybaldiyeva, Aizhan; Kaldarova, Mira
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6855-6861

Abstract

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
Forecasting stock market prices using deep learning methods Ismailova, Aisulu; Beldeubayeva, Zhanar; Kadirkulov, Kuanysh; Doumcharieva, Zhanagul; Konyrkhanova, Assem; Ussipbekova, Dinara; Aripbayeva, Ainura; Yesmukhanova, Dariga
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5601-5611

Abstract

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.
Co-Authors Aripbayeva, Ainura Ayazbaev, Talgatbek Doumcharieva, Zhanagul Ismailova, Aisulu Kadirkulov, Kuanysh Kaldarova, Mira Kodanova, Shynar Konyrkhanova, Assem Satybaldiyeva, Aizhan Ussipbekova, Dinara Utenova, Balbupe Uzakkyzy, Nurgul Yesmukhanova, Dariga