Tag: feature extraction

AdaptoNet achieved 99 percent accuracy on a 101-speaker speech dataset and demonstrated robust generalization on the AMI Meeting Corpus using ...

A new hierarchical feature attention network reconstructs high-resolution images from low-resolution inputs while keeping the model below one million parameters.

A study in Discover Artificial Intelligence reports that a BOA-CNN-LSTM-enhanced SIMI-Motion system cut relay handover feature extraction errors from 13.2 ...

A newly published two-stage deep network called FADFNet uses parallel feature extraction, source-image compensation, and a decomposition feedback loop to ...

Researchers developed a two-stage random forest framework that classifies faults in Sallen–Key and Chebyshev analog filter circuits with about 98.5% ...

Researchers combined a deep autoencoder with K-Means clustering to group brain tumor MRI scans into latent imaging-pattern clusters without any ...

A new YOLO-based framework called GSDD-Net detects subtle gear surface defects in cluttered industrial images with higher accuracy, a smaller ...

Researchers have reimagined pixels as three-dimensional cuboids whose sizes are set by RGB values, using the empty space between them ...

Researchers have developed MFKR Net, a lightweight deep learning model that identifies individuals from contactless middle finger knuckle images with ...

Researchers in China have developed a content-guided attention network that restores color and detail in underwater images through efficient fusion ...