Fossil Classification using Machine Learning

Abstract: Fossil classification is a critical task in paleontology, aiding in the identification and categorization of ancient life forms. Traditional classification methods rely on manual inspection, which is time-consuming and prone to human error. This research presents an automated fossil classification system using machine learning, specifically deep learning models such as Convolutional Neural Networks (CNNs). The system is trained on a diverse dataset of fossil images, leveraging advanced image processing techniques and data augmentation to improve classification accuracy. The evaluation results demonstrate that deep learning-based classification significantly outperforms traditional methods in terms of accuracy, efficiency, and scalability. This study highlights the potential of artificial intelligence in revolutionizing fossil classification and providing valuable insights for scientific and industrial applications.This project provides an efficient, accurate, and scalable solution for paleontological studies, reducing dependency on manual classification. This project presents a machine learning approach to fossil classification using deep neural networks. The system processes fossil images and categorizes them into predefined fossil classes. The model is trained using a dataset of labeled fossil images and evaluated based on accuracy and classification performance. The proposed system aims to improve the speed and accuracy of fossil identification compared to traditional manual methods. Fossil classification plays a crucial role in paleontology, helping researchers identify and categorize fossil specimens. Traditional classification methods are time-consuming and require expert knowledge.