Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

The key challenge in credit card fraud detection lies in the imbalance between legitimate and fraudulent transactions. Fraud ...

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving ...

Advancements in whole-genome sequencing have revolutionized plant species characterization, providing a wealth of genotypic data for analysis. The combination of genomic selection and neural networks, ...