Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

According to A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges, published in Sensors, AI-based predictive maintenance systems ...

ABSTRACT: Cognitive impairment is a frequent and debilitating outcome of stroke, profoundly affecting patient independence, recovery trajectories, and long-term quality of life. Despite its prevalence ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...

Many carbon-rich meteorites contain ingredients commonly found in life, but no evidence of life itself. James St. John, CC BY When NASA scientists opened the sample return canister from the OSIRIS-REx ...

Amirali Aghazadeh receives funding from Georgia Tech. When NASA scientists opened the sample return canister from the OSIRIS-REx asteroid sample mission in late 2023, they found something astonishing.

You’re managing a federal agency contact center when someone calls in with a bomb threat targeting a government building. The employee immediately begins working to obtain as much information from the ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...