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 …

Deception detection is gaining increasing interest due to ethical and security concerns. This
paper explores the application of convolutional neural networks for the purpose of …

The internal structure and operation mechanism of large-scale language models are
analyzed theoretically, especially how Transformer and its derivative architectures can …

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

This paper presents a novel contribution to the field of regional style transfer. Existing
methods often suffer from the drawback of applying style homogeneously across the entire …

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

Image-text matching is a key multimodal task that aims to model the semantic association
between images and text as a matching relationship. With the advent of multimedia …

In the ever-evolving landscape of social network advertising, the volume and accuracy of
data play a critical role in the performance of predictive models. However, the development …

Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods
in the context of 3D scene reconstruction, this study contrasts these modern approaches with …

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 …