Researchers have explored the potential of deep neural networks (DNNs ... To improve the model's accuracy and generalization, the team augmented the data with random mixtures of essential oil ...

Researchers have developed a transfer learning-enhanced physics-informed neural network (TLE-PINN) for predicting melt pool morphology in selective laser melting (SLM). This novel approach combines ...

Alongside R1 and R1-Zero, DeepSeek today open-sourced a set of less capable but more hardware-efficient models. Those models ...

Rourkela: Researchers at the National Institute of Technology (NIT), Rourkela, have developed an AI-based Multi-Class Vehicle ...

Deep neural networks, brain-inspired machines often used to study how actual brains function, are much worse at image generalization than we are. But why? How can such models be improved?

The world of machine learning is evolving rapidly, and choosing the right framework for training models can significantly ...

In a significant breakthrough, researchers have developed an advanced explainable deep learning model to predict and analyze harmful algal blooms (HABs) in freshwater lakes and reservoirs across China ...

This article establishes a neural network-based technique for automatic peak picking in 2D NMR spectroscopy, demonstrating a ...

Morphological profiling allows accurate identification of cell types in dense iPSC-derived cultures, allowing its use for quality control and differentiation monitoring.