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2023 - 20242
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Bölüm: Işık University, School of Graduate Studies, Master’s Program in Computer Engineering × Konu: Derin öğrenme ×

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  • Küçük Resim
    Yayın
    Image super resolution using deep learning techniques
    (Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-09-02) El Ballouti, Salah Eddine; Eskil, Mustafa Taner; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer Engineering
    Image SR using Deep Learning Techniques has become a critical area of research, with significant progress in improving image quality and detail. This thesis examines and contrasts eight advanced deep learning-based SR methods: CARN, EDSR, ESPCN, RCAN, RDN, SRCNN, SRGAN, and VDSR, using the DIV2K dataset. The evaluation covers multiple aspects to offer a thorough understanding of each method's effectiveness, efficiency, and structure. Performance measurements such as PSNR and SSIM are utilized for evaluating the fidelity of super-resolved images. Computational efficiency is evaluated based on inference time and memory requirements. Training time is analyzed, taking into account the speed of convergence for training on the DIV2K dataset. Model complexity is examined, exploring architectural details such as network depth, and the integration of specialized elements like residual blocks and attention mechanisms. Additionally, the thesis explains in a clear and detailed manner the trade-offs between performance and complexity, discussing whether more complex architectures deliver significantly better results compared to simpler models and whether the computational cost justifies the improvements. Finally, a qualitative comparison is conducted to emphasize the strengths and weaknesses of each technique. Through this comprehensive analysis, this thesis offers insights into the field of deep learning-based image SR, assisting researchers and practitioners in choosing the most appropriate method for various applications.
  • Küçük Resim
    Yayın
    Supervised decision making in forex investment using ML and DL classification methods
    (Işık Üniversitesi, 2023-07-20) Jiroudi, Abdullah; Eskil, Mustata Taner; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer Engineering
    The suggested trading system offers an approach that takes into account the complexity and high trading volume of the foreign exchange (FX0) market. Its main objective is to address the challenges faced by traders in the GBP/JPY currency pair and assist them in making quick decisions. To achieve this, machine learning and deep learning techniques are integrated to propose a trading algorithm. The proposed algorithm works by combining data from different time intervals. The Long Short-Term Memory (LSTM) model is used to predict indicator values, while the XGBoost classifier is employed to determine trading decisions. This method aims to adapt to rapidly changing patterns in the forex market and enables the detection of subtle changes in price dynamics through a sliding window training approach. Experiments conducted have shown promising results for the suggested trading system. Positive outcomes have been obtained in terms of capital growth and prediction accuracy. However, since this method is highly risky and requires further development in terms of risk management, the inclusion of risk management techniques and algorithm optimization is targeted. This study contributes to the improvement of trading strategies while bridging the gap between researchers and traders. It also demonstrates the potential of machine learning and deep learning techniques to enhance decision-making processes in financial markets. This trading system offers traders a range of advantages. The utilization of machine learning and deep learning techniques enables rapid analysis of large amounts of data and decision-making capabilities. Additionally, by combining data from different time intervals, it becomes possible to evaluate long-term trends and short-term fluctuations more effectively. In conclusion, the suggested trading system empowers traders to be competitive in the forex market and achieve better outcomes. Furthermore, it contributes to the increased utilization of machine learning and deep learning techniques in financial markets and encourages further research in the field.