This project will follow 78 Very Low Birth Weight (VLBW) infants previously enrolled in a R21 grant through the age of 4 years. The data gathered over 6 weeks of the NICU stay includes multiple factor

NIAID Data Ecosystem30

Performance comparison of machine learning algorithms for land cover classification

土地覆盖分类

机器学习算法性能评估

This dataset consists of shapefiles of training points names (SamplePoints.zip) which were collected on the Google Earth Engine platform for our land cover analysis. The code.zip contains the codes

Mendeley Data20

Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape

土地覆盖分类

机器学习算法性能评估

Mapping land cover (LC) in mountainous regions, such as the Gilgit-Baltistan (GB) area of Pakistan, presents significant challenges due to complex terrain, limited data availability, and accessibility

NIAID Data Ecosystem30

Descriptive characteristics of dataset.

儿童健康预测

WaSH干预效果

Objective Child stunting continues to pose a substantial global health challenge, requiring multifaceted strategies that combine conventional epidemiological approaches with advanced analytic methods.

NIAID Data Ecosystem20

Performance of RF and NN algorithms on child morbidity.

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