Implementation and analysis of computational intelligence methodologies across neural networks, fuzzy logic systems, and genetic algorithms, featuring 9 complete projects with systematic evaluation.
Directory: Genetic_Algorithm/
1 Project: Evolutionary computation for optimization
- Genetic Algorithm for Feature Selection - Customer classification optimization
Directory: Fuzzy_Logic_Systems/
3 Projects: Intelligent fuzzy inference systems for real-world applications
- Intelligent Fitness Recommendation System - Personalized workout optimization
- Fuzzy Logic Irrigation Control Systems - Environmental decision making
- Intelligent Fuzzy Student Performance Prediction - Academic outcome prediction with ML pipeline
Directory: Deep_Learning_Neural_Networks/
5 Projects: Progressive neural network implementations using CIFAR-10 dataset
- Logistic Regression from Scratch - Binary classification with pure NumPy
- Single Hidden Layer Neural Network - Basic neural network fundamentals
- Multi-Class Neural Network - Complete CIFAR-10 classification
- Deep Neural Network Framework - Modular framework with multiple optimizers
- CNN Architecture Comparison - Advanced convolutional networks with regularization
- Neural Networks: From-scratch implementations (NumPy) and framework-based solutions (TensorFlow/Keras)
- Fuzzy Logic: scikit-fuzzy implementations with interactive decision interfaces
- Genetic Algorithms: Custom evolutionary optimization with fitness-based selection
- Evaluation: Performance analysis across classification accuracy, optimization convergence, and decision quality metrics
pip install numpy pandas matplotlib seaborn scikit-learn tensorflow scikit-fuzzy gradio
Course: Computational Intelligence
University: University of Isfahan
Professor: Dr. Hossein Karshenas
Semester: Spring 2025