The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts
of data, increasing the need for effective anomaly detection mechanisms to ensure system …

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 …

Autonomous vehicles'(AVs) capacity to identify and categorise things in real-time is crucial to
their development for the sake of efficient and safe navigation. Because of its exceptional …

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

The exponential growth of data in recent years has necessitated the development of more
efficient and scalable machine learning algorithms to handle large-scale data analysis …

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 …

Fraud detection in financial networks presents a significant challenge due to the complexity
and volume of transactions. Traditional detection methods often struggle with scalability and …

The increasing complexity of urban traffic networks demands more intelligent and adaptive
solutions for traffic management. This paper presents a novel approach utilizing Deep …