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Multi-modal Depression Estimation Based on Sub-attentional Fusion
Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide....
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Multimodal Depression Detection Network Based on Emotional and Behavioral Features in Conversations
Early detection of depression has always been a challenge. Currently, research on automatic depression detection mainly focuses on using low-level...
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Depression screening using hybrid neural network
Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive...
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Computational Intelligence in Depression Detection
According to the World Health Organisation, depression is the prime contributor to mental disability worldwide. Depression is a severe threat to...
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Early depression detection using ensemble machine learning framework
Social media platforms typically serve as generators of huge data sources as users express their sentiments directly or indirectly on these...
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Depression detection using cascaded attention based deep learning framework using speech data
Efficient detection of depression is a challenging scenario in the field of speech signal processing. Since the speech signals provide a better...
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Hybrid HAN Model to Investigate Depression from Twitter Posts
Depression is currently one of the most concerning complications in the world. With the availability of social networking sites, human openly express...
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Emotion detection for supporting depression screening
Depression is the most prevalent mental disorder in the world. One of the most adopted tools for depression screening is the Beck Depression...
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Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities
Social media has become a safe space for discussing sensitive topics such as mental disorders. Depression dominates mental disorders globally, and...
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Hybrid machine learning models to detect signs of depression
Depression is a prevalent mental illness that can only be diagnosed through self-reporting. Unfortunately, 70% of individuals do not seek medical...
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Depression Recognition Based on Pre-trained ResNet-18 Model and Brain Effective Connectivity Network
Depression has emerged as a primary health burden globally. Therefore, effectively identifying depression has become a significant challenge and...
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Depression detection based on social networking sites using data mining
Social networking is becoming increasingly prevalent in today's globe. Young folks, senior citizens, and the general public use social media....
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Multi-view graph-based interview representation to improve depression level estimation
Depression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years....
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An Investigation of Video Vision Transformers for Depression Severity Estimation from Facial Video Data
Recognising depression from facial expressions and movements in video data using machine learning models has gained considerable attention in recent...
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Mental Health Monitoring Using Deep Learning Technique for Early-Stage Depression Detection
An electroencephalogram, often known as an EEG, can detect neuronal activity by analysing the electrical currents that are generated within the brain...
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Reading Between the Frames: Multi-modal Depression Detection in Videos from Non-verbal Cues
Depression, a prominent contributor to global disability, affects a substantial portion of the population. Efforts to detect depression from social...
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Depression detection and subgrou** by using the active and passive EEG paradigms
Depression, a paramount global health challenge, necessitates an advanced diagnostic approach. This study employs EEG and AI on a...
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Combining Informative Regions and Clips for Detecting Depression from Facial Expressions
Artificial intelligence methods are widely applied to depression recognition and provide an objective solution. Many effective automated methods for...
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CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms
Depression is a serious mental health condition that affects a person’s ability to feel happy and engaged in activities. The COVID-19 pandemic has...
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Depression Detection on Twitter Using RNN and LSTM Models
Social media mainly provides an unparalleled chance to detect depression early in young adults. Depression is an illness that so often requires the...