AI, when properly implemented, has the power to transform emergency medicine by enhancing patient outcomes, reducing ...

Despite various mitigation strategies, most existing methods rely on static adjustments that fail to account for individual ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random neighborhoods ...

To develop a machine learning (ML) algorithm to predict survival probabilities for patients with epithelial ovarian cancer (EOC).Data were obtained from the SEER database for women diagnosed with EOC ...

Bhubaneswar: The buffer zone of Similipal Biosphere Reserve is most affected in forest fires with most incidents linked to ...

AI-driven models for predicting concrete compressive strength show promise, with machine learning techniques improving accuracy in SCM formulations.

We use Machine Learning algorithms, specifically Decision Trees and Random Forests, to build predictive models and evaluate their performance. 🌳 1. Decision Trees A Decision Tree is a supervised ...

Random Forest consistently outperformed Decision Trees in all three case studies. XGBoost and LightGBM improved model accuracy through advanced boosting techniques.

Polymer research shows that machine learning can optimize tribological performance of epoxy coatings, with GBR outperforming ...

Data was cleaned and normalized, excluding metabolites below detection limits, and advanced ML models—including deep learning (DL), random forest (RF), and support vector machines (SVM)—were employed.