This research paper examines the potential of artificial intelligence (AI) in strengthening data
security and mitigating the growing threat of cyber-attacks. As digital threats continue to …

Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet
they often face challenges such as poor generalization and oscillation issues. To counter …

Accurate stock market predictions following earnings reports are crucial for investors.
Traditional methods, particularly classical machine learning models, struggle with these …

This paper advances a novel architectural schema anchored upon the Transformer
paradigm and innovatively amalgamates the K-means categorization algorithm to augment …

Accurate prediction of US Treasury bond yields is crucial for investment strategies and
economic policymaking. This paper explores the application of advanced machine learning …

Optimization theory serves as a pivotal scientific instrument for achieving optimal system
performance, with its origins in economic applications to identify the best investment …

Gastrointestinal (GI) tract disorders, ranging from benign polyps to aggressive forms of
cancer, pose significant health challenges globally. Early detection and precise …

Graph convolutional networks (GCNs) have effectively utilized local connections for point
cloud analysis. How-ever, capturing distant dependencies (ie, global features) with a single …

Currently, widely used first-order deep learning optimizers include non-adaptive learning
rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM …

In this paper, Pro-HRnet-CNN, an innovative model combining HRNet and void-convolution
techniques, is proposed for disease prediction under lung imaging. Through the …