Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/7

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Transaction Types in Cryptocurrencies
(Institute of Electrical and Electronics Engineers Inc., 2024) Uysal, T.; Unozkan, H.
The cryptocurrency ecosystem has witnessed an explosive growth, and this study delves into the mechanisms that fuel this expansion. Focusing on airdrops, staking, farming, and coin burning, the research analyzes a vast dataset of transactions from the Ethereum and Binance Networks. This analysis sheds light on the strategic use of these tools by highlighting transactions that engage users and distribute rewards (e.g., airdrops, staking). Furthermore, the study investigates farming as a method to enhance market efficiency by providing liquidity, and coin burning as a strategy to manage token supply and potentially increase value through scarcity. While effective utilization of these mechanisms can bolster token value and project success, regulatory challenges remain. Ultimately, this study aims to raise public awareness of cryptocurrency transaction types and the associated risks. Although many researchers have studied illicit flows on cryptocurrency transactions, in literature we haven't confronted with any study regarding transaction types. By analyzing over 107 million transaction records, the research presents the distribution of these transaction types. With the analysis of the transaction types, the definitions of them, the statistical outputs from the more than 107 million transaction records from ERC20 and BEP20 blockchain systems and the analysis of some specific token types such as Pancake Swap and Shiba, this research is the first study to present an academic approach with statistical analysis. This study can provide investors with knowledge that safeguard them from the potential pitfalls of cryptocurrency transactions. Also, this study presents some basic statistics so as to understand main patterns of different types of transactions. © 2024 IEEE.
Citation - Scopus: 1
Unet3D Based Next Frame Prediction;
(Institute of Electrical and Electronics Engineers Inc., 2024) Akbacak, E.
The concept of next-frame prediction, which is predicting the subsequent frames using historical frames' spatial and temporal properties, is indispensable in computer vision. There are various application of frame prediction such as predicting a future event in autonomous vehicles, predicting patient falls in biomedical engineering, and reducing the amount of data transmitted in video transmission. Deep learning applications in this field are the focus of the most effective methods. Especially CNN-LSTM, Convolutional LSTMs, and GAN-supported deep learning methods are very common. This study proposes the inflated 3D Unet encoder-decoder model, which is not yet used for the next-frame prediction problem. The proposed model predicts both the next frame and the subsequent frames. Experimental results have shown that the proposed method gives better results than CNN-LSTM and Convolutional LSTMs. © 2024 IEEE.

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Browsing Scopus İndeksli Yayınlar Koleksiyonu by Journal "8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423"