An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals.

Affective Computing is one of the central studies for achieving advanced human-computer interaction and is a popular research direction in the field of artificial intelligence for smart healthcare frameworks. In recent years, the use of electroencephalograms (EEGs) to analyze human emotional states has become a hot spot in the field of emotion recognition. However, the EEG is a non-stationary, non-linear signal that is sensitive to interference from other physiological signals and external factors.

Efficacy, safety and cost-effectiveness of vonoprazan vs Proton Pump Inhibitors in reflux disorders and H. pylori eradication: A literature review.

Gastroesophageal reflux disease (GERD) is one of the most prevalent conditions worldwide and is conventionally treated by proton pump inhibitor therapy. However, around 40% of people have reported some form of resistance to this therapy. Vonoprazan has recently been approved for the treatment of GERD.

A Comprehensive Survey of Depth Completion Approaches.

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map.

Contrastive Learning for 3D Point Clouds Classification and Shape Completion.

In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other related applications. Our idea is to add contrastive learning into AutoEncoders to encourage global feature learning of the point cloud classes.