A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...

Atomic force microscopy faces enduring challenges in imaging speed, accuracy, and automated analysis as microstructure characterization becomes increasingly ...

How to become a data scientist Want to start a career as a data scientist? Learn how to become a data scientist with career ...

White Blood Cell Classification is a deep learning project built with Python, TensorFlow, and Keras that classifies five types of WBCs from microscopic images using a CNN model. With advanced image ...

Machine-learning models identify relationships in a data set (called the training data set) and use this training to perform operations on data that the model has not encountered before. This could ...

Division of Applied Chemistry, Faculty of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

A unified ML management system requires careful orchestration of multiple components, from experiment tracking with MLflow to model serving with FastAPI. Interactive ...

Abstract: Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition ...