This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ...

The paper presents a hybrid machine learning model for the spatial segmentation of soils by salinity using multispectral satellite data from Sentinel-2 and climate parameters of the ERA5-Land model. The proposed method aims to solve the problem of accurate soil cover segmentation under climate change and high spatial heterogeneity of data. The approach includes the sequential ...

This study delves into the emerging opportunities and challenges arising from the integration of education and artificial intelligence in the unique backdrop of the COVID-19 pandemic. Its primary objective is to develop an optimized ensemble model that sheds light on the surge in learning engagement among secondary school students during Emergency Distance Learning (EDL) ...