54 citations · 127 across the 6 of their papers we have counts for
6 papers
SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection
Jingxuan Zhou, Yuehao Wu, Yibo Zhang +4
Aiming at the problem of difficulty in accurately identifying graphical implicit correlations in multimodal irony detection tasks, this paper proposes a Semantic Irony Recognition…
Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation
Delun Lai, Yeyubei Zhang, Yunchong Liu +2
This paper introduces a novel deep learning-based multimodal fusion architecture aimed at enhancing the perception capabilities of autonomous navigation robots in complex environme…
Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
Yeyubei Zhang, Zhongyan Wang, Zhanyi Ding +5
Social media has become an important source for understanding mental health, providing researchers with a way to detect conditions like depression from user-generated posts. This t…
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang +5
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation.…
A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases
Yunchong Liu, Xiaorui Shen, Yeyubei Zhang +4
Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evalua…
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
Yuchen Cao, Jianglai Dai, Zhongyan Wang +4
The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through use…