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Training and activation functions  \n  - Data division ratios (train/validation/test)  \n  - Model generalization and overfitting  \n- Confusion matrix generation and performance visualization  \n- Graphical User Interface (GUI) for interactive model training and classification  \n\n---\n\n## 🧩 **Technologies**\n\n- **MATLAB**  \n- **Deep Learning Toolbox**  \n- **Image Processing Toolbox**\n\n---\n\n## 📊 **Results**\n\nThe best models achieved **100% accuracy** on training datasets and strong generalization across different image sets.  \nFurther experiments revealed the impact of training parameters and dataset diversity on classification performance.\n\n---\n\n## 🖥️ **Graphical Application**\n\nAn interactive MATLAB GUI allows the user to:\n- Configure network parameters (neurons, layers, functions)\n- Train or import models\n- Load or draw new images\n- Classify shapes and visualize results\n\n---\n\n## 📚 **About**\n\nDeveloped by **Ana Rita Pessoa** and **João Francisco Claro** (ISEC, 2024/2025).  \nThis project explores neural networks for image-based classification, bridging concepts of **machine learning**, **pattern recognition**, and **data analysis**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritap03%2Fneuralnetwork-shapeclassifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fritap03%2Fneuralnetwork-shapeclassifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritap03%2Fneuralnetwork-shapeclassifier/lists"}