TensorScan AI Whitepaper
  • 📊Executive Summary
  • 🌎Introduction
  • Project Overview
    • 🚀Project Overview
    • ⚙️Utilizing Bittensor for Decentralized Computing Power
    • 🤖AI-Driven Wallet Analysis
    • 🌐Browser Extension and Etherscan Integration
  • Tokenomics
    • 💰TensorScan AI Tokenomics
  • AI-Driven Wallet Analysis Architecture
    • 🤖AI-Driven Wallet Analysis Architecture
    • 🔄Data Ingestion and Preprocessing Module
    • 🛠️Feature Engineering and Dataset Preparation
    • 🤖Machine Learning Models
    • ⚙️Decentralized Computation with Bittensor
    • 🌐User Interface and Integration
  • Target Audience
    • 🎯Target Audience
    • 💼Casual and Serious Investors
    • 🔎Crypto Analysts and Researchers
    • 📶Traders
    • 👩‍💻Blockchain Developers
    • 🦍DeFi and NFT Communities
  • Challenges and Solutions
    • ⚙️Challenges and Solutions
    • 💻Challenge 1: High Computational Demand
    • 🔄Challenge 2: Dynamic and Evolving Data
    • 🚀Challenge 3: User Accessibility and Engagement
  • 🗺️Roadmap
Powered by GitBook
On this page
  1. AI-Driven Wallet Analysis Architecture

Decentralized Computation with Bittensor

  • Model Training and Inference: Leverages the decentralized computing resources of Bittensor to distribute the computational load of training AI models and processing real-time wallet transaction analysis. This ensures scalability and efficiency, as the system can tap into a global network of nodes for processing power.

  • Continuous Learning: The architecture supports continuous learning, where the models are regularly updated with new transaction data, adapting to changing patterns in wallet behavior over time.

PreviousMachine Learning ModelsNextUser Interface and Integration

Last updated 1 year ago

⚙️