In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in
healthcare predictive analytics to improve the speed and precision of illness detection …

Due to the quantization noise created by analog-to-digital converters (ADCs) with just one
bit of resolution, signal identification in Multiple-Input Multiple-Output (MIMO) systems is a …

As businesses become more dependent on networked digital devices, advanced persistent
threats (APT) attacks are progressively becoming a component of the threat landscape …

The U-Net architecture is a convolutional neural network model that is well-known for its
skills in biomedical image segmentation. This study investigates the revolutionary potential …

Ensuring agricultural production and food security relies on the early and precise
identification of crop diseases. It may be tedious, error-prone, and time-consuming to use …

This research investigates the application of Random Forest algorithms to enhance disease
prediction within healthcare analytics. Using large healthcare datasets, the research …

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool
for regression and classification problems. When using support vector machines (SVMs), the …

APE, PTR, and DHRA, three revolutionary techniques in the Adaptive Mobile Health
Framework (AMHF), empower patients and improve chronic illness management. These …