Discover how explainable AI enhances Parkinson’s disease prediction with improved accuracy and clinical interpretability.
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 ...
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 ...
Anisha Jadhav's SPX framework revolutionizes Agile story point estimation. This AI breakthrough, published by IEEE, offers ...
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 ...
Analysis of the 191 samples shows that 55 percent of groundwater falls within low to no restriction categories for irrigation ...
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, ...
This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine ...
Recent research is advancing seismic hazard modeling through AI-driven soil liquefaction prediction, interpretable machine learning, physics-based simulations, and waveform-based probabilistic ...
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