Abstract: Human action recognition has a wide range of application scenarios, including monitoring and autonomous driving, among others. Traditional methods rely on single-modal information to extract ...

Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, ...

Abstract: Human activity recognition (HAR) constitutes a significant subfield of artificial intelligence, addressing diverse problems such as improving human health and advancing innovative technology ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

Automated Machine Learning (AutoML) aims to streamline the end-to-end process of ML models, yet current approaches remain constrained by rigid rule-based frameworks and structured input requirements ...

Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...