In this paper, an image recognition algorithm based on the combination of deep learning
and generative adversarial network (GAN) is studied, and compared with traditional image …

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

The objective of this research is to enhance the efficiency of intelligence acquisition through
sentiment analysis of public opinion, a crucial element of open-source intelligence, utilizing …

This paper introduces a sophisticated deep learning model designed to predict high-risk
behaviors in financial traders by analyzing vast amounts of transaction data. The model …

Zero sample learning is an effective method for data deficiency. The existing embedded zero
sample learning methods only use the known classes to construct the embedded space, so …

This paper delves into leveraging neural networks for equity market forecasting by
amalgamating gated recurrent units (GRUs) with an attention paradigm to refine the …

The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of
financial fraud detection by synergistically combining the advantages of graph neural …

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major
histocompatibility complex molecules (MHC) is fundamental to the immune response …

Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models,
especially in areas where security is critical. However, traditional OOD detection methods …

This paper explores the application of deep learning and multimodal information fusion to
enhance image recognition capabilities. Multimodal fusion, integrating information from …