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 ...

RetinaDA unites six public fundus sets into a 512 × 512 macula-centered benchmark with built-in domain gaps, enabling ...

The seven-month programme is aimed at working professionals seeking to build production-ready artificial intelligence ...

A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen content without relying on labor-intensive field measurements.