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Yayın Extracting meaningful information student surveys with NLP(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-01-29) Pourjalil, Kajal; Ekin, Emine; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringThis thesis applied NLP techniques to analyze and summarize bilingual student feedback collected via end-of-semester surveys. The dataset, which contained open-ended responses in both English and Turkish, required a model adept at preserving linguistic nuances across languages. The Llama 2-7b-hf model, which had been trained explicitly for text generation, was selected for its capability to produce coherent and contextually relevant summaries. Data preprocessing involved organizing metadata such as department, semester, course name, and section number, segregating comments by word count, and removing personal identifiers to ensure privacy. Shorter comments (fewer than ten words) were grouped and summarized using a pipeline from the Transformers library, while longer comments were fine-tuned with metadataspecific prompts for detailed summarization. To further enhance analysis, sentiment classification was performed using the “cardiffnlp/twitter-robertabase-sentiment” model, categorizing feedback into negative, neutral, and positive sentiments. Evaluation metrics included expert reviews, contextual relevance, and logical consistency with the dataset’s sentiment distribution. Compared to previous models, the Llama 2 model demonstrated superior performance in generating complete, coherent summaries while preserving the overall intent and tone of the comments. Ultimately, this research highlighted the effectiveness of LLMs in processing multilingual educational data and their potential to provide actionable insights for improving course content and student experiences.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 EngineeringImage 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.Yayın Federated hybrid privacy-preserving movie recommendation system for internet-of-vehicles(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-08-02) Şimşek, Musa; Erman Tüysüz, Ayşegül; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringIn this research, we introduced a pioneering strategy to address the pressing privacy concerns associated with vehicular movie recommendation systems. As the demand for personalized entertainment options in vehicles increases, so does the need to protect user data. To tackle this challenge, we utilized the PyTorch framework to create a robust foundation from scratch. A key component of our approach was the addition of Laplace noise during the training process, which ensured differential privacy. This technique effectively safeguarded user data while simultaneously optimizing model performance, allowing us to maintain high levels of recommendation accuracy. Furthermore, we employed the Optuna hyperparameter optimization framework, which played a crucial role in enhancing the model's performance. By fine-tuning various parameters, we were able to elevate the overall efficiency of the system beyond the capabilities of the base model. Our extensive experimentation utilized the Movielens-1M benchmark movie dataset, which provided a solid basis for evaluating our approach. The results demonstrated a significant improvement over baseline models, validating the effectiveness of our privacy-preserving vehicular movie recommendation system. In addition to our centralised model, we conducted a comprehensive comparison with practical federated frameworks, including FedAvg, FedProx, and FedMedian. Our findings revealed that all federated models outperformed the centralised models by at least 2%, while also exhibiting shorter runtimes.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 EngineeringThe 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.
