Active learning represents a transformative paradigm in machine learning, aimed at reducing the annotation burden by selectively querying the most informative data points. This approach leverages ...

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring irradiance sensors during operation, using routine meteorological data instead.

Deep learning techniques can enhance diagnosis of Meniere disease (MD) and severity grading, according to a study published ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Scientists have developed a geometric deep learning method that can create a coherent picture of neuronal population activity during cognitive and motor tasks across experimental subjects and ...

Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...

An innovative and scalable proximity labelling method profiled proteins present in the Caenorhabditis elegans brain during learning, identifying known regulators as well as novel biological pathways.

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

When kids tinker in the classroom, they get to build many useful skills from computing to collaboration to creativity and more. Krithik Ranjan, PhD student and member of the ACME Lab, studies low-cost ...