Yıldız, Beytullah

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Yıldız, Beytullah
B.,Yildiz
Yildiz, B
B., Yildiz
B., Yıldız
Beytullah, Yildiz
Y.,Beytullah
Yildiz,B.
Y., Beytullah
Yıldız,B.
Beytullah, Yıldız
Yildiz, Beytullah
B.,Yıldız
Job Title
Doçent Doktor
Email Address
beytullah.yildiz@atilim.edu.tr
Main Affiliation
Software Engineering
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Website
ORCID ID
Scopus Author ID
14632851900
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WoS Researcher ID
Scholarly Output

15

Articles

6

Citation Count

35

Supervised Theses

4

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2021 - 20235
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Type: Conference Object

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Developing and Evaluating a Model-Based Metric for Legal Question Answering Systems
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bakir,D.; Yildiz,B.; Aktas,M.S.; Software Engineering
    In the complicated world of legal law, Question Answering (QA) systems only work if they can give correct, situation-aware, and logically sound answers. Traditional evaluation methods, which rely on superficial similarity measures, can't catch the complex accuracy and reasoning needed in legal answers. This means that evaluation methods need to change completely. To fix the problems with current methods, this study presents a new model-based evaluation metric that is designed to work well with legal QA systems. We are looking into the basic ideas that are needed for this kind of metric, as well as the problems of putting it into practice in the real world, finding the right technological frameworks, creating good evaluation methods. We talk about a theory framework that is based on legal standards and computational linguistics. We also talk about how the metric was created and how it can be used in real life. Our results, which come from thorough tests, show that our suggested measure is better than existing ones. It is more reliable, accurate, and useful for judging legal quality assurance systems. © 2023 IEEE.
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    Conference Object
    Citation - Scopus: 17
    Improving Text Classification With Transformer
    (Institute of Electrical and Electronics Engineers Inc., 2021) Soyalp,G.; Alar,A.; Ozkanli,K.; Yildiz,B.; Software Engineering
    Huge amounts of text data are produced every day. Processing text data that accumulates and grows exponentially every day requires the use of appropriate automation tools. Text classification, a Natural Language Processing task, has the potential to provide automatic text data processing. Many new models have been proposed to achieve much better results in text classification. The transformer model has been introduced recently to provide superior performance in terms of accuracy and processing speed in deep learning. In this article, we propose an improved Transformer model for text classification. The dataset containing information about the books was collected from an online resource and used to train the models. We witnessed superior performance in our proposed Transformer model compared to previous state-of-art models such as L S T M and CNN. © 2021 IEEE
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    Conference Object
    Citation - WoS: 0
    Citation - Scopus: 1
    A Novel Use of Reinforcement Learning for Elevated Click-Through Rate in Online Advertising
    (Ieee Computer Soc, 2023) Haider, Umair; Yildiz, Beytullah; Software Engineering
    Efficiently predicting Click-through Rate (CTR) is crucial for the success of online advertising. Traditional methods often struggle to adapt to the dynamic nature of user preferences and the evolving relevance of advertisements. In this study, we propose a novel Reinforcement Learning (RL) approach for CTR prediction, leveraging OpenAI Gym and the Thompson Sampling algorithm. Our approach dynamically estimates CTR, cleverly adapting to the ever-changing landscape of user preferences and advertisement relevance. Results showcase the exceptional performance of Thompson Sampling in CTR prediction, sur-passing other RL methods with a remarkable 10% higher confidence level. This emphasizes the significant potential of our RL approach in optimizing the selection of online advertisements.
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    Conference Object
    Citation - Scopus: 2
    Enhancing Image Resolution With Generative Adversarial Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Yildiz,B.; Software Engineering
    Super-resolution is the process of generating high-resolution images from low-resolution images. There are a variety of practical applications used in real-world problems such as high-definition content creation, surveillance imaging, gaming, and medical imaging. Super-resolution has been the subject of many researches over the past few decades, as improving image resolution offers many advantages. Going beyond the previously presented methods, Generative Adversarial Networks offers a very promising solution. In this work, we will use the Generative Adversarial Networks-based approach to obtain 4x resolution images that are perceptually better than previous solutions. Our extensive experiments, including perceptual comparison, Peak Signal-to-Noise Ratio, and classification success metrics, show that our approach is quite promising for image super-resolution. © 2022 IEEE.
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    Conference Object
    Citation - Scopus: 1
    Reinforcement Learning for Intrusion Detection
    (Springer Science and Business Media Deutschland GmbH, 2023) Saad,A.M.S.E.; Yildiz,B.; Software Engineering
    Network-based technologies such as cloud computing, web services, and Internet of Things systems are becoming widely used due to their flexibility and preeminence. On the other hand, the exponential proliferation of network-based technologies exacerbated network security concerns. Intrusion takes an important share in the security concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solving the intrusion problem and ensuring the secure delivery of network-based technologies and services. In this paper, we propose a novel approach using deep reinforcement learning to detect intrusions to make network applications more secure, reliable, and efficient. As for the reinforcement learning approach, Deep Q-learning is used alongside a custom-built Gym environment that mimics network attacks and guides the learning process. The NSL-KDD dataset is used to create the reinforcement learning environment to train and evaluate the proposed model. The experimental results show that our proposed reinforcement learning approach outperforms other related solutions in the literature, achieving an accuracy that exceeds 93%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.