RamanChain

📘 Description

RamanChain is a next-generation blockchain focused on efficiency, security, and scalability, integrating advanced Artificial Intelligence (AI) and Machine Learning (ML) technologies. This blockchain uses an innovative consensus mechanism called Proof of Mathematic Integrity (PoMI), based on Ramanujan's formulas, and an alternative Proof of Learning (PoL) mechanism, which applies Federated Learning to maximize the computational power of miners.

🚀 Main Features

• Proof of Mathematic Integrity (PoMI): Validates blocks through complex mathematical equations, adjusting complexity based on network data.
• Proof of Learning (PoL): Miners collaborate to train AI models, earning rewards for their participation.
• Staking and Rewards: Staking system for Arc and Ranj coins, with rewards based on staking time and amount.
• Self-Healing Smart Contracts: Detects and corrects vulnerabilities automatically.
• Intelligent Stablecoins: Adjusts value automatically based on AI algorithms.
• Fraud Detection: AI algorithms identify suspicious behavior.
• Energy Consumption Optimization: Adjusts complexity to minimize consumption.
• Automated Portfolio Management: AI manages cryptocurrency portfolio allocations to maximize user returns.

📂 Project Structure

ramanchain/
│
├── blockchain/                 # Main blockchain module
│   ├── block.py                # Block definition and manipulation
│   ├── transaction.py          # Transaction system
│   ├── staking.py              # Staking and rewards management
│   ├── wallet.py               # Wallet and balance management
│   ├── audit.py                # Transaction audit system
│   ├── consensus.py            # PoMI consensus mechanism
│   ├── proof_of_learning.py    # Federated Learning-based PoL consensus
│   ├── self_healing_contract.py# Self-healing smart contracts
│
├── ai/                         # AI and ML algorithms
│   ├── energy_optimizer.py     # Energy consumption optimization
│   ├── transaction_fee_optimizer.py # Transaction fee optimization via Q-Learning
│   ├── fraud_detection.py      # Fraud and anomaly detection
│   ├── portfolio_manager.py    # Automated portfolio management
│   ├── load_predictor.py       # Load prediction using LSTM
│   ├── market_manipulation_detector.py # Market manipulation detection
│   ├── stablecoin_manager.py   # Stablecoin management
│   ├── liquidity_pool_manager.py # Intelligent liquidity pool management
│
├── interfaces/                 # Graphical interface
│   ├── ui.py                   # User interface
│
├── monitoring/                 # Real-time monitoring
│   ├── metrics.py              # Monitoring with Prometheus
│
├── scripts/                    # Test scripts
│   ├── load_test.py            # Load and performance tests
│
├── main.py                     # Main entry point
└── README.md                   # Project documentation

📋 Requirements

To run the project, install the following dependencies:

• Python 3.8+
• Python Libraries:

  rsa, numpy, scikit-learn, tensorflow, torch, prometheus_client, tkinter
  

Install with:

pip install rsa numpy scikit-learn tensorflow torch prometheus_client

▶️ How to Run

1. Clone the repository:

   git clone https://github.com/marciolscoutinho/ramanchain.git
   cd ramanchain

2. Start the project:

   python main.py

🧩 Key Components

1. Blockchain:

   • Blocks: Store transactions and mathematical equations used in PoMI.
   • Transactions: Manages sending/receiving with digital signatures.
   • Staking: Staking system for rewards, using AI to optimize.

2. Integrated AI and ML:

   • Fraud Detection: Algorithms identify suspicious network activities.
   • Fee Optimization: Dynamic fee adjustment via Q-Learning.
   • Load Prediction: LSTM networks predict behaviors to optimize.

3. User Interface (UI):

   • Visualizes balances, transaction history, staking details, and audit monitoring.

4. Real-Time Monitoring:

   • Uses Prometheus to monitor performance metrics, like transaction count and processing time.

🧪 Testing

Run load and performance test scripts:

python scripts/load_test.py

👥 Contribution

Contributions are welcome! Follow these steps to contribute:

1. Fork the repository.
2. Create a branch for your changes:

   git checkout -b my-feature

3. Commit your changes:

   git commit -m 'Adding new feature'

4. Push to the branch:

   git push origin my-feature

5. Open a Pull Request.

📄 License

This project is licensed under the MIT License.

📬 Contact

For questions or more information, contact: [[email protected]].

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This README has been formatted for easy readability and to provide a clear understanding of the RamanChain project for developers and collaborators.