Background The growing population of patients with adult congenital heart disease (ACHD) present complex lifelong care needs ...
Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...
If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is ...
The review reports that blockchain-enhanced federated learning systems typically achieve slightly lower raw accuracy than ...
The Mobile Rundown on MSN
He built a learning game at 16 that millions of students now use
He launched a learning game at 16 that now reaches millions of students worldwide. Here’s what we can learn from this young ...
Cui, J.X., Liu, K.H. and Liang, X.J. (2026) A Brief Discussion on the Theory and Application of Artificial Intelligence in ...
As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...
How AI, privacy-preserving computation, and explainable models quietly strengthen payments, protect data, and bridge traditional finance with crypto systems.
Apple’s “App Intents” and Huawei’s “Intelligent Agent Framework” allow the OS to expose app functionalities as discrete ...
Incubated for DoD & Intelligence use cases, startup announces commercial availability of enterprise AI platform, delivering ...
Federated Learning 1 Authors, Creators & Presenters: Phillip Rieger (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Kavita Kumari (Technical University of ...
🎯 Project Vision This project demonstrates a Federated Lakehouse Architecture where multiple compute engines can seamlessly work with different catalog systems over a unified data storage layer ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback