There is a disconnect between the promise logistics leaders see in Agentic AI and their readiness to deploy it at scale as they start 2026, according to a new survey from ORTEC. While nearly all ...

While demand planning accuracy currently hovers around 60%, DLA officials aim to push that baseline figure to 85% with the help of AI and ML tools. Improved forecasting will ensure the services have ...

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

Penske Logistics has deployed a new AI platform from Augment after a successful pilot, aiming to improve load management and ...

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

Understanding the derivative of the cost function is key to mastering logistic regression. Learn how gradient descent updates weights efficiently in machine learning. #MachineLearning ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

ML powered system that predicts most suitable crop using ensemble(hard voting) of Decision Tree, Random Forest, and Gradient Boosting models implemented from scratch ...

The problem of Reels video addiction is on the rise, particularly among young people. Through the use of machine learning, this study aims to predict Reels video addiction. The dataset used in this ...

We developed a classifier to infer acute ischemic stroke severity from Medicare claims using the modified Rankin Scale at discharge. The classifier can be used to improve stroke outcomes research and ...

1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia 2 InnoV'COM Laboratory-Sup'Com, University of Carthage, Ariana, Tunisia ...