Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

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 ...

LCGC International’s interview series on the evolving role of artificial intelligence (AI)/machine learning (ML) in separation science continues with Boudewijn Hollebrands from Unilever Foods R&D, ...

An AI model with the potential to transform cervical spondylosis diagnosis by spotting subtle vertebral changes quickly and accurately.

Integrating deep learning with traditional forecasting techniques can improve early warning systems by capitalizing on each approach’s respective advantages.

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 ...

Visualization of attention maps from the residual attention networks for the inspiratory convolutional neural network (I-CNN) and expiratory convolutional neural network (E-CNN) models. Attention maps ...

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 ...

The earliest stage of drug discovery is governed by a simple constraint: there are far more possible drug-like molecules than any pharmaceutical laboratory could ever test. A new deep learning system, ...