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Abstract: This research describes how we implemented machine learning technology to help detect malicious participants on the Ethereum blockchain, including fake smart contracts and honeypots.

The secondary raw materials market is growing and the demand is real. The opportunities are massive, but growth without trust creates casualties. Blockchain and smart contracts do not eliminate risk; ...

This is a Streamlit-based web application that generates Ethereum smart contracts with customizable parameters. The application provides an intuitive interface for creating various types of smart ...

The Bills have a little more salary-cap space to work with after restructuring Josh Allen's deal. Justin Edmonds / Getty Images Before the NFL’s 2026 league year begins Wednesday afternoon, the Bills ...

U.S. prescription drugs market project to grow from $634 billion market to $883 billion by 2030 according to Grandview Research [1] Global blockchain in healthcare market is projected to grow from $11 ...

Smart contracts are the backbone of decentralized applications, decentralized finance (DeFi), and blockchain ecosystems. Unlike traditional software, once deployed, smart contracts are immutable, ...

Once considered a far-off invention of the future, smart cities exist today as a way to improve and automate systems like transportation and energy management. Although smart cities are most often ...

Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a ...

According to a Fortune Business Insights report, the global smart manufacturing market is projected to reach $998.99 billion by 2032. The exciting aspect about this growth is that it is not merely ...