Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …

Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …

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

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

Depression is a common and serious mood disorder that negatively affects the patient's
capacity of functioning normally in daily tasks. Speech is proven to be a vigorous tool in …

Effective and efficient automatic depression diagnosis is a challenging subject in the field of
affective computing. Since speech signals provide useful information for diagnosing …

Depression is a critical problem in modern society that affects an estimated 350 million
people worldwide, causing feelings of sadness and a lack of interest and pleasure …

As a common mental disorder, depression becomes a major threat to human health and
may even heavily influence one's daily life. Considering this background, it is necessary to …

With the rapid rise of information transmission via the Internet, efforts have been made to
reduce network load to promote efficiency. One such application is semantic computing …

Objectives This study aimed to develop a classification model to detect and distinguish
apathy and depression based on text, audio, and video features and to make use of the …