Adversarial examples—images subtly altered to mislead AI systems—are used to test the reliability of deep neural networks.

For individuals and organisations alike, the cybersecurity of computer networks is becoming increasingly important as digital infrastructure becomes more ubiquitous. Cyber threats are increasingly ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

A comprehensive comparative study of deep learning architectures for Human Activity Recognition (HAR) using the UCI-HAR dataset. This research evaluates the performance of CNN-LSTM, attention ...

ABSTRACT: Phishing attacks remain a pervasive threat in the cybersecurity landscape, necessitating intelligent and scalable detection mechanisms. This paper suggests a deep learning-based method for ...

We are excited to share our first big milestone in solving a grand challenge that has hampered the predictive power of computational chemistry, biochemistry, and materials science for decades. By ...

Background: Diabetic retinopathy (DR) screening faces critical challenges in early detection due to its asymptomatic onset and the limitations of conventional prediction models. While existing studies ...

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...