AI-assisted signal debugging has broad impact across many domains.
Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control.
We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...
Donation after circulatory death (DCD) procurements provide an opportunity to alleviate the limited organ supply for solid ...
Under the influence of global warming, the Arctic is transitioning from a state dominated by multi-year thick ice to a "New ...
Monoclonal antibody (mAb) manufacturing must continually improve to keep up with increasing demands. To do this, biomanufacturers can deploy machine learning tools to augment traditional process ...
The CT-based whole-lung radiomic nomogram accurately identifies AECOPD and offers a robust tool for clinical diagnosis and treatment planning.
Explore how artificial intelligence and digital innovations are transforming sludge dewatering in wastewater systems, ...
A machine learning model using routine lab data at 3 months postdiagnosis accurately predicted mortality or liver transplant risk in autoimmune hepatitis.
Machine learning models using initial neuropsychological and neuropsychiatric clinical data accurately distinguished AD from bvFTD.
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses the kernel matrix inverse (Cholesky ...
This Jupyter Notebook (thompson_cell_plan_project.ipynb) implements a machine learning pipeline to predict customer cancellations of cell phone plans. The project involves data loading, exploration, ...
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