Build a Convolutional Neural Network (CNN) that recognizes traffic signs.

This project focuses on multi-class image classification using CNNs with the CIFAR-10 dataset. It compares a baseline and an enhanced model to classify 10 categories, including trucks, for real-world applications like preventing deer-vehicle collisions. Includes architecture, training, and evaluation insights.

A machine learning program in Java that makes multi-class image classifications

Multi-class network intrusion detection using CatBoost with GPU/CPU support, handling highly imbalanced datasets. Includes stratified downsampling, K-Fold cross-validation, and explainable AI with SHAP feature importance analysis. Designed for high-performance training on large datasets with detailed evaluation metrics and visualizations.