AI-Powered Image Recognition

This study investigates the application of AI-powered image recognition systems utilizing Convolutional Neural Networks (CNNs) and transfer learning. Leveraging benchmark datasets (ImageNet, CIFAR-10, MNIST), we evaluate model accuracy, precision, recall, and F1-score. Our findings reveal that deep learning architectures, especially transfer learning models like ResNet50 and InceptionV3, achieve high accuracy in object classification. However, concerns about data bias and interpretability remain. This paper emphasizes ethical deployment and outlines pathways for improving fairness and robustness in image recognition systems.