Class unbalanced datasets are frequently encountered in a variety of areas including health, security, and finance. Often these datasets create bias in the supervised learning models trained for the prediction task. One of the most successful techniques to handle imbalanced data is...

The insurance industry has changed rapidly over the last few decades. One factor in this change is the continuous growth of massive amounts of data that need to be processed properly to be optimally utilized. This has led to a strong wave of advanced processing technologies that can...

Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle...

Sentiment analysis is crucial in understanding and analyzing public opinions, feedback, and social media data. In this study, we propose a modified Bayesian Boosting algorithm with weight-guided optimal feature selection for sentiment analysis. The goal is to improve the accuracy and efficiency...

Executing complex and time-sensitive operations has become difficult due to the increased acceptance of Internet of Things (IoT) devices and IoT-generated big data, which can result in problems with power consumption and lag time. The fog computing layer is a distributed computing option that...

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast...