Accurate classification of wetland vegetation is essential for biodiversity conservation and carbon cycle monitoring. This study developed an adaptive ...

Abstract: Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named ...

Abstract: Context: Ensemble methods are powerful machine learning algorithms that combine multiple models to enhance prediction capabilities and reduce generalization errors. However, their potential ...

The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by security ...

Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

This study addresses the challenges of uncertainty in wave simulations within complex and dynamic ocean environments by proposing a reinforcement learning-based model ensemble algorithm. The algorithm ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...

The year 2024 is the time when most manual things are being automated with the assistance of Machine Learning algorithms. You’d be surprised at the growing number of ML algorithms that help play chess ...

The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to ...