Mental illness is highly prevalent nowadays, constituting a major cause of distress in
people's life with impact on society's health and well-being. Mental illness is a complex multi …

Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …

With the acceleration of the pace of work and life, people are facing more and more
pressure, which increases the probability of suffering from depression. However, many …

Video anomaly detection is of critical practical importance to a variety of real applications
because it allows human attention to be focused on events that are likely to be of interest, in …

Users can interact with one another through social networks (SNs) by exchanging
information, delivering comments, finding new information, and engaging in discussions that …

The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in
automated mental health analysis. However, existing relevant studies bear several …

Multimodal sentiment analysis and depression estimation are two important research topics
that aim to predict human mental states using multimodal data. Previous research has …

Detecting depression based on non-verbal behaviors has received great attention.
However, most prior work on detecting depression mainly focused on detecting depressed …

In this study, we analyze the use of state-of-the-art technologies for speaker recognition and
natural language processing to detect Alzheimer's Disease (AD) and to assess its severity …

Depression is a leading cause of disability with tremendous socioeconomic costs. In spite of
early detection being crucial to improving prognosis, this mental illness remains largely …