Large language models (LLMs) have been widely recognised as transformative artificial generative intelligence (AGI) technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.
For BRADY we find F1-scores of 0. 75 using our framework compared to 0. 50 for the video based rater clinicians, while for PIGD we find 0. 78 for the framework and 0. 45 for the video based rater clinicians.
Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics of the environments.
First, we present a human pose based fall representation which is invariant to appearance characteristics.
Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements.
Ranked #5 on Knowledge Distillation on ImageNet
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.
On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.
Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG).