Artificial Intelligence

The dataset includes five subclasses of white blood cells: eosinophils, basophils, neutrophils, monocytes, and lymphocytes. The types and positions of white blood cells in the images have been manually and accurately annotated using an annotation tool, and the annotation information files are stored in .txt, .xml, and .csv formats to meet the format requirements of different network model training for annotation files.

The Universal Networking Language (UNL) is a pioneering framework designed to facilitate seamless communication and knowledge sharing across different languages and cultures. This UNL French Dictionary focuses specifically on the rich and diverse world of French cuisine, offering a structured representation of culinary terms, ingredients, cooking techniques, and descriptors in French alongside their universal equivalents.

Purpose and Importance:

The UNL French Dictionary serves several key purposes:

The dataset has undergone format conversion based on URPC2021_Sonar_images_data, enabling it to be trained by YOLO and RT-DETR models.

 The folder 'images' contains image files

The folder 'labels' contains TXT format annotation files.

The annotation file in the folder annotations is in XML format

Data.yaml is the configuration file for YOLO training

Data_deTR is the configuration file for RT-DETR and US-DETR training

The SINEW (Sensors in Home for Elderly Wellbeing) dataset consists of 15 high-level biomarker features, derived from raw sensor readings collected by in-home sensors used for predictive modeling research: SINEW 15 - Monthly Biomarker.

This dataset was collected for a study focused on the early detection of mild cognitive impairment, providing an opportunity for timely intervention before it progresses to Alzheimer's disease.