https://github.com/ralolooafanxyaiml/neural-evolution-engine
A modular AI evolution simulation engine built with Python and TensorFlow. Uses Deep Learning to predict organism adaptations against environmental threats.
https://github.com/ralolooafanxyaiml/neural-evolution-engine
artificial-intelligence deep-learning evolution-simulation keras machine-learning neural-networks numpy oop pandas python scikit-learn simulation tensorflow
Last synced: 27 days ago
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A modular AI evolution simulation engine built with Python and TensorFlow. Uses Deep Learning to predict organism adaptations against environmental threats.
- Host: GitHub
- URL: https://github.com/ralolooafanxyaiml/neural-evolution-engine
- Owner: ralolooafanxyaiml
- License: mit
- Created: 2025-11-20T18:47:17.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-11-21T21:29:58.000Z (2 months ago)
- Last Synced: 2025-11-21T22:15:32.732Z (2 months ago)
- Topics: artificial-intelligence, deep-learning, evolution-simulation, keras, machine-learning, neural-networks, numpy, oop, pandas, python, scikit-learn, simulation, tensorflow
- Language: Python
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Evolution Engine V3.0: Multi-Modal AI Simulation
### Project Overview
**Neural Evolution Engine V3.0** is an advanced Deep Learning application built from scratch using **TensorFlow** and **Keras**. It simulates evolutionary biology principles by predicting the optimal adaptation strategy for a species when faced with catastrophic environmental threats.
**What's New in V3.0? (The Cognitive Leap!)**
V3.0 features a revolutionary **Triple-Branch Multi-Modal Architecture** that handles **three distinct tasks**: processing **Biological Data**, analyzing **Environmental Imagery**, and providing **Natural Language assistance** via a chatbot.
---
### Technical Architecture (Triple-Core Brain)
The system utilizes a sophisticated architecture that combines two main model structures for prediction and one for conversational AI.
#### 1. Simulation Core (Hybrid Prediction)
| Branch | Architecture | Input | Function |
| :--- | :--- | :--- | :--- |
| **Visual Branch (The "Eye")** | CNN (2x Conv2D + MaxPooling) | 64x64 RGB Images | Analyzes visual patterns (snow, fire, toxic waste) to identify the threat. |
| **Biological Branch (The "Brain")** | ANN (Dense Layers) | Encoded Biological Features | Processes organism's physiological constraints and traits. |
| **Fusion Layer** | Concatenate + Softmax | Merged CNN/ANN features | Generates probability distribution for 6 evolutionary outcomes. |
#### 2. Chatbot Core (NLP / Knowledge Assistant)
| Branch | Architecture | Input | Function |
| :--- | :--- | :--- | :--- |
| **Language Branch** | **LSTM (Recurrent Neural Network)** | User Text (Tokenized, Embedded) | Understands questions about Evolutionary Biology (e.g., "What is genetic drift?"). |
| **Function** | **Intent Classification** | Chatbot Model (`evolutionchatbotmodel.h5`) | Provides relevant, pre-trained biological explanations. |
---
### Usage: Hybrid Interaction Modes
When running `main.py`, the user is presented with **THREE MAIN OPTIONS**:
| Option | Mode | Primary AI Used | Description |
| :---: | :--- | :--- | :--- |
| **1** | **Simulation Mode (Hybrid)** | **CNN + ANN** | Predicts the optimal evolutionary adaptation based on visual threats and organism traits. |
| **2** | **AI Assistant Mode** | **LSTM (NLP)** | Answers user queries regarding evolutionary concepts and biological definitions. |
| **3** | **Quit** | - | Exits the program. |
---
### Tech Stack
* **Deep Learning:** TensorFlow, Keras (Functional API)
* **Sequence Modeling:** **LSTM (New!)**
* **Computer Vision:** OpenCV (Image Preprocessing)
* **Data Engineering:** Pandas, NumPy
* **Preprocessing:** Scikit-Learn (StandardScaler, LabelEncoder)
---
### Installation & Run
1. **Clone the Repository**
```bash
git clone [https://github.com/ralolooafanxyaiml/neural-evolution-engine]
cd Neural-Evolution-Engine
pip install tensorflow pandas numpy scikit-learn opencv-python
```
2. **Train the NLP Chatbot Model (One Time Setup)**
```bash
python chatbot_train.py
```
3. **Run the Main Engine**
```bash
python main.py
```
Data Sources & Acknowledgements
This project utilizes external datasets for training the Visual Threat Detection (CNN) module:
Intel Image Classification by Puneet Bansal (Cold/Ice)
Natural Disaster Images by Aseem Arora (Heat/Fire)
Garbage Classification by Sashaank Sekar (Toxin/Pollution)
Underwater Image Classification by Great Sharma (Airless/Aquatic)
US Drought Data (Scarcity)
Developed by Mustafa İlker Aktaş - Global AI Contributor