Deep Learning Approaches for Dog Breed Identification and Behaviour Analysis in Conversational AI Systems

This research study aims to develop an innovative system for dog breed and behavior identification using deep learning algorithms, integrated with ChatGPT API and Google Text-to-Speech (TTS) for enhanced user interaction. Leveraging advanced convolutional neural networks (CNNs) such as ResNet, Inception, and VGG16, the system accurately identifies dog breeds based on visual input. These models are trained on a comprehensive dataset of dog images to ensure high accuracy and robust performance in breed classification. This multi-faceted approach not only streamlines the process of identifying dog breeds and behaviors but also enhances user experience through seamless interaction and accessibility features. The CNN models ResNet, Inception, and VGG16 demonstrated their effectiveness in identifying dog breeds, with accuracies of $87 \%, 85 \%$, and $83 \%$ respectively. The project showcases the potential of combining state-of-the-art deep learning models with advanced AI and TTS technologies to create an intelligent, user-friendly system for dog enthusiasts, trainers, and veterinarians.