Explainable AI (XAI) exists to close this gap. It is not just a trend or an afterthought; XAI is an essential product capability required for responsibly scaling AI. Without it, AI remains a powerful ...

Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

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 ...

Cognitive computing represents an innovative frontier within computer science, merging artificial intelligence, machine learning, and computational ...

This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest ...

Exploring How Generative AI, Edge AI, and Quantum Machine Learning Are Revolutionizing Healthcare, Finance, Logistics, and ...

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, ...

Researchers have developed a highly accurate, explainable machine learning model that can differentiate between essential tremor (ET) and cortical myoclonus (CM) using wearable sensor data. A new ...

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...