2 citations · 7 across the 9 of their papers we have counts for
9 papers
Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention
Jingyuan Yi, Peiyang Yu, Tianyi Huang +1
This work proposes an LSTM-based sentiment classification model with multi-head attention mechanism and TF-IDF optimization. Through the integration of TF-IDF feature extraction an…
Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies
Tianyi Huang, Jingyuan Yi, Peiyang Yu +1
The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLa…
A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
Tianyi Huang, Zeqiu Xu, Peiyang Yu +2
In this paper, we propose an optimized Transformer model that integrates Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and apply it to fake news classifica…
Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media
Jiani Wang, Xiaochuan Xu, Peiyang Yu +1
Detecting and classifying cyberbullying in social media is hard because of the complex nature of online language and the changing nature of content. This study presents a multi-sta…
Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions
Jingyuan Yi, Zeqiu Xu, Tianyi Huang +1
The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic insti…
A Hybrid Attention Framework for Fake News Detection with Large Language Models
Xiaochuan Xu, Peiyang Yu, Zeqiu Xu +1
With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Lan…
Enhancing User Intent for Recommendation Systems via Large Language Models
Xiaochuan Xu, Zeqiu Xu, Peiyang Yu +1
Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and C…
The Application of Large Language Models in Recommendation Systems
Peiyang Yu, Zeqiu Xu, Jiani Wang +1
The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of…
Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm
Jingyuan Yi, Peiyang Yu, Tianyi Huang +1
Aiming at the latest particle swarm optimization algorithm, this paper proposes an improved Transformer model to improve the accuracy of heart disease prediction and provide a new…