To reduce the serious losses caused by debris flows, debris-flow susceptibility mapping (DFSM) is valuable and indispensable. The objective of this study was to build a high-quality regional debris flow prediction model through a heterogeneous ensemb... read more
Computer vision is an important field of artificial intelligence that enables machines to interpret and understand visual information from images. It is the basis of automated visual understanding in smart systems. Monitoring of streets, recognizing ... read more
Exploring large language models (LLMs) performance in the specific medical domain can help understand their generalizability in real-world application. We assessed the predictive and decision-support value of two state-of-the-art LLMs in predicting b... read more
Climate variability is vital for effective climate adaptation and risk management. This study investigates the temperature variations during the Boro season in Bangladesh and evaluates the performance of multiple machine learning models for predictin... read more
In healthcare, Digital Twin (DT) model can create real-time digital representations of patients' physiological states, enabling intelligent, data-driven decision-making for continuous monitoring and personalized treatment-without the invasiveness of ... read more
Drug response prediction (DRP), accounting for the diverse biological characteristics of cancer types that affect sensitivity or resistance to treatment, is crucial for anticancer drug selection and discovery. Although numerous deep learning models f... read more
BACKGROUND: Current physical activity guidelines recommend 150-300 min/week of moderate-to-vigorous physical activity (MVPA) to improve health. However, whether brief, sporadic MVPA can be included within this recommended dose remains unclear. METHOD... read more
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.