Electronic Design

Scaling Industrial AI with Zero-Touch Deployment

Industrial AI deployment traditionally requires onsite ML specialists and custom models per location. Five strategies ...

Anthropic Claude wants to be your helpful colleague, always looking over your shoulder

Anthropic has acknowledged that users may have trouble coming up with ways to employ Claude by publishing a list of suggested ...
Journal of Medical Internet Research

Longitudinal Model Shifts of Machine Learning–Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals

Objective: This study aimed to assess the impact of a model shift on ML-based prediction models by evaluating 3 different use cases—delirium, sepsis, and acute kidney injury (AKI)—from 2 hospitals (M ...
Open Heart

Machine learning-based cardiovascular risk calculator for non-cardiac surgery

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
IEEE

Digital In-Memory Compute for Machine Learning Applications With Input and Model Security

Abstract: Digital in-memory compute (IMC) architectures allow for a balance of the high accuracy and precision necessary for many machine learning applications, with high data reuse and parallelism to ...
IEEE

Model-Free Q-Learning for Output Feedback Nash Strategy of Decentralized Nonzero-Sum Games

Abstract: In this article, we present a model-free output feedback (OPFB) Q-learning algorithm to find the optimal Nash equilibrium strategy for the decentralized control problem (DCP) of nonzero-sum ...
Journal of Medical Internet Research

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. To ...