EurekAlert!

An interpretable deep learning modeling architecture considering process underlying logics reveals a promising way to the intelligent chemical industry

Intelligent manufacturing is one of the most important strategies towards the upgradation of chemical industry. The deep learning modeling technology acts as a crucial role in this industry ...

Flood disaster prediction using multi-scale deep learning and neuro-fuzzy inference

A new algorithmic framework that can predict flooding could help save lives and reduce the devastation as climate change ...

Interpretable machine learning to accelerate nanocatalyst discovery

A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures.
News Medical

Guidelines for using interpretable machine learning methods in computational biology

Machine learning is a powerful tool in computational biology, enabling the analysis of a wide range of biomedical data such as genomic sequences and biological imaging. But when researchers use ...
EurekAlert!

An interpretable machine learning model for predicting 5‐year survival in breast cancer based on integration of proteomics and clinical data

Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on ...
JSTOR Daily

A robust and interpretable end-to-end deep learning model for cytometry data

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using ...
WRAL TechWire

Duke professor – a critic of ‘runaway train’ AI development – is named one of the field’s top women

DURHAM – Cynthia Rudin, a professor at Duke University, is one of the top 10 women in the field of artificial intelligence research and development, reports AI Magazine. She is “known for her ...