The proposed framework combines machine learning and earthquake engineering to prioritize high-risk buildings, optimizing ...

The role of machine learning and deep learning in wildfire prediction remains limited by geographic concentration, uneven ...

The financial landscape of 2026 is defined by a paradox: machine learning systems are now more powerful and autonomous than ever, yet they operate under the strictest regulatory scrutiny in history.

Abstract: The exponential growth of sophisticated cyber threats has necessitated the evolution from traditional static honeypots to intelligent, adaptive deception systems. This systematic literature ...

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 ...

ABSTRACT: Heart disease continues to be a major global cause of death, making the development of reliable prediction models necessary to enable early detection and treatment. Using machine learning to ...

Abstract: In this work, we propose a novel approach that uses an ensemble of deep neural networks (DNN) and convolutional neural networks (CNN) to predict the drag coefficient of small, ...

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 ...