Aipowercoin

Analysis of AI agent economy governance challenges, proposing Agentbound Tokens and blockchain-based solutions for autonomous value exchange systems.

Analysis of AI agents in electricity markets using cryptocurrency payments, strategic resource allocation between consumption and transaction verification, and equilibrium outcomes in post-human economies.

Comprehensive analysis of blockchain and AI integration in 6G networks, covering secure services, IoT applications, spectrum management, and future research directions.

A novel blockchain framework that replaces traditional proof-of-work with reinforcement learning, enabling energy-efficient consensus while training AI models across distributed nodes.

Review of blockchain and AI applications for enabling prosumers in smart grids, covering energy markets, peer-to-peer trading, and operational optimization for net zero emissions.

Exploring how blockchain technology enhances AI transparency, addresses the black box problem, and ensures accountability in high-stakes sectors through immutable audit trails.

Exploring how Artificial Intelligence and Blockchain create chaordic economic systems through the balance of chaos and order in business and society.

Research analyzing machine learning algorithms for cryptocurrency price prediction using 1,681 cryptocurrencies and comparing gradient boosting with LSTM models for trading strategy performance.

Comprehensive study analyzing cryptocurrency time-series complexity using entropy measures and forecasting performance of statistical, machine learning, and deep learning models.

A decentralized framework for optimizing AI service placement, selection, and routing in mobile networks, addressing the tradeoff between service quality and latency while supporting user mobility.

Research on using Graph Neural Networks for detecting vulnerabilities in educational blockchain smart contracts through bytecode analysis and control flow graphs.

Advanced framework combining PINNs, Digital Twins, and Blockchain for real-time energy optimization in smart buildings with 35% cost reduction and 97.7% accuracy.