Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...

The ModelSpy attack system reconstructs deep learning architectures from GPU electromagnetic emissions at up to six meters, ...

New AGI lab aims to revolutionize machine learning with symbolic models, moving beyond traditional deep learning.

As the electric vehicle (EV) market surges, the biggest anxiety for owners and manufacturers remains the battery. How long ...

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to patient outcomes using widely available bulk RNA sequencing data. The approach ...

One of the most puzzling aspects of common chronic inflammatory skin diseases such as psoriasis is how they become chronic. What allows an ongoing condition to stay dormant for months or even years, ...

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...

The deep learning model developed by researchers at the University of Pennsylvania identified severe heart dysfunction far more effectively than traditional AI methods. It also diagnosed 39 cardiac ...