相关论文: Blockchain Transaction Fee Forecasting: A Comparis…

In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his…

Ethereum's Gas mechanism attempts to set transaction fees in accordance with the computational cost of transaction execution: a cost borne by default by every node on the network to ensure correct smart contract execution. Gas encourages…

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in Ethereum Virtual Machine (EVM), making it a global…

Ethereum is a distributed blockchain that can execute smart contracts, which inter-communicate and perform transactions automatically. The execution of smart contracts is paid in the form of gas, which is a monetary unit used in the…

Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations…

Ethereum is one of the most popular platforms for the development of blockchain-powered applications. These applications are known as Dapps. When engineering Dapps, developers need to translate requests captured in the front-end of their…

The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a…

Smart contracts are programs stored and executed on a blockchain. The Ethereum platform, an open-source blockchain-based platform, has been designed to use these programs offering secured protocols and transaction costs reduction. The…

The gas fee, paid for inclusion in the blockchain, is analyzed in two parts. First, we consider how effort in terms of resources required to process and store a transaction turns into a gas limit, which, through a fee, comprised of the base…

In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's…

We present the main concepts, components, and usage of GASOL, a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. GASOL offers a wide variety of cost models that allow inferring the gas consumption associated to selected…

Blockchain technology shows significant results and huge potential for serving as an interweaving fabric that goes through every industry and market, allowing decentralized and secure value exchange, thus connecting our civilization like…

Accurate prediction of price behavior in the foreign exchange market is crucial. This paper proposes a novel approach that leverages technical indicators and deep neural networks. The proposed architecture consists of a Long Short-Term…

Given the low throughput of blockchains like Bitcoin and Ethereum, scalability - the ability to process an increasing number of transactions - has become a central focus of blockchain research. One promising approach is the parallelization…

This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and…

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more…

Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it unhackable and therefore, more secure than the traditional paper-based or…

This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in…

In this note, we compare Bitcoin trading performance using two machine learning models-Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM)-and two technical analysis-based strategies: Exponential Moving Average…

Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and…