When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...

Palmprint recognition has quietly become one of the most reliable biometric technologies in the world, but a team of Algerian ...

In this paper, explainable artificial intelligence (XAI) based prediction is used for gate all around (GAA) MOSFET to predict its electrical behaviour. The data set is produced using TCAD simulations ...

Researchers trained an explainable XGBoost model on 1,670 field-recorded concrete mixes, achieving R-squared 0.9263 in ...

AI systems have tremendous potential, but the average user has little visibility and knowledge on how the machines make their decisions. AI explainability can build trust and further push the ...

Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...

Using a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month ...

Are deep learning models truly untrustworthy? Or are we simply governed by the naive intuition that 'what cannot be explained ...

Optimized AI models could help estimate concrete compressive strength in wastewater environments and support future ...

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights—including a model-estimated probability score for ...

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, ...