Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
The team utilized machine learning to analyze public data from the National Health and Nutrition Examination Survey.
One of the promises of AI is that it can reduce workloads so employees can focus more on higher-value and more engaging tasks ...
An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
TensorFlow was created simply to develop your own machine-learning (ML) models. You might even experience it daily and not know it, like recommendation systems that suggest the next YouTube video, ...
Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control. AI-driven robotics and digital twins are closing the gap between simulation and ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
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