Water contamination by heavy metals remains one of the most pressing global environmental challenges, particularly in regions undergoing rapid industrialization. Among these pollutants, cobalt ions ...

Explainable Artificial Intelligence (XAI) seeks to render the operation and decisions of complex machine learning systems transparent and interpretable to users, regulators and other stakeholders. As ...

Medulloblastoma the most common malignant pediatric brain tumor with a high risk of metastasis and poor survival outcomes. To delineate the metastatic microenvironment,, researchers in China have ...

QuadSci, the most predictive and prescriptive AI for customer intelligence, has been selected as the winner of the 2026 Machine Learning Company of the Year award in the 9th annual AI Breakthrough ...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care This study aims to investigate the impact of ...

The conversion of carbon dioxide into clean fuels is regarded as an important route toward carbon neutrality. CO 2 methanation, in particular, has drawn increasing interest due to its favorable ...

This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...

Krishna, Satyapriya, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Transactions on ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

A machine learning (ML)-based model may aid in-hospital community-acquired pneumonia (CAP) mortality prediction, according to study findings published in Respiratory Medicine. Res ...