Intelligent depression detection and support system: Statistical analysis, psychological review and design implication

Tasnim, Mashrura; Shahriyar, Rifat; Nahar, Nowshin; Mahmud, Hossain

doi:10.1109/healthcom.2016.7749494

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…Expecting the students' sentiments are wanted to the activities done in homeroom, their excited state should be visible in the event that they are deterred or not, and considering this the teacher can help the student by zeroing in more earnestly on that particular student. [11] In the event that different appearances in a comparable scene show a comparable positive or negative assessment, it would help with figuring out the whole situation of the scene, whether or not subjects in the scene are lively or whether something wrong is happening in the scene [12].…”

Section: Literature Surveymentioning

confidence: 99%

AI Based Mental Health Prediction System

Desai¹,

Nagapurkar²,

Gajbhiye³

et al. 2023

IJRASET

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The most common mood disorder in the world, depression has a considerable negative influence on health and functionality as well as profound personal, familial, and society implications. The correct and timely identification of depressionrelated symptoms may have numerous advantages for both doctors and those who are affected. The current work aimed to develop and clinically test a system capable of identifying visual signs of melancholy and supporting physician decisions. Programmable suffering assessment based on visible signals is a rapidly expanding research area. Picture handling and AI computations are the focus of the current thorough evaluation of existing approaches as described in more than sixty distributions during the last 10 years. The current datasets, various information-gathering methods, and visual cues of misery are compiled. The survey depicts estimates for visual element extraction, dimensionality reduction, layout and relapse choosing options, as well as numerous combination techniques. Incorporating a quantitative meta-analysis of announced results based on execution metrics risk-tolerant, it identifies general trends and significant irksome issues to be taken into account in ongoing investigations of programmed sadness appraisal using visible signs either alone or in combination with obvious signals. Additionally, the proposed work used deep learning to predict the level of the downturn as shown by the contribution of current face photographs.

show abstract

Section: Literature Surveymentioning

confidence: 99%

AI Based Mental Health Prediction System

Desai¹,

Nagapurkar²,

Gajbhiye³

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Expecting the students' sentiments are expected to the activities done in homeroom, their stimulated state should be clear in the event that they are deterred or not, and considering this the teacher can help the student by zeroing in more truly on that particular student. [5] On the off chance that different appearances in a commensurate scene show a basically indistinguishable positive or negative assessment, it would help with figuring out the whole situation of the scene, whether subjects in the scene are energetic or whether something wrong is happening in the scene [6].…”

Section: Literature Surveymentioning

confidence: 99%

AI-Based Mental Health Prediction System

2023

IRJMETS

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Sadness, the most predominant temperament problem around the world, unfavorably affects well-being and usefulness as well as huge mental, familial, and cultural implications. For the two clinicians and patients who are impacted, there might be different advantages to the exact and provoking location of side effects associated with melancholy. The ebb and flow research intends to make and clinically assess a framework that could perceive visual signs of despairing and help clinical decisions. A quick creating area of exploration is the programmable evaluation of experiencing in view of detectable signs. The ongoing complete evaluation of existing techniques as expressed in excess of sixty appropriations during the most recent 10 years is centered around picture handling and simulated intelligence calculations. Visual signs of hopelessness, different datagathering methods, and the present datasets are collected. Gauges for visual component extraction, dimensionality decrease, design, and backslide choice options are displayed in the review, alongside instances of a few different mix methodologies.

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“…The suggested model can increase detection review over 6-9 percent in F1-score, according to experimental data. By delving deeper into the data on social interactions, the number of social network system with poor connections is over 14 percent greater among stressed users than among non-stressed users, showing that stressed peoples friends are less linked and difficult than non-stressed people's friends [11].…”

Section: B System Frameworkmentioning

confidence: 99%

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

Shinde¹,

Kulkarni²

2021

IJRTE

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Now a day’s supported visual and audio cues for automatic depression assessment may be a fast emerging research subject. This comprehensive evaluation of existing methodologies focuses on machine learning (ML) algorithm and image processing (IP) algorithm, as documented in over sixty articles over the last ten years. There is a visual indicator of depression, several data collection procedures are used, and finally examined the previous year or existing datasets. In this article describes techniques and algorithms as well as methods for dimensionality reduction, visual feature extraction, regression approaches, and classification decision procedures, and also various fusion tactics. A significant meta-analysis of published data is given, based on performance indicators that are robust to chance, to identify general trends and important pressing concerns for further research using visual and verbal cues alone or in combination with signals for automated depression evaluation The suggested work also used deep learning and natural language processing to estimate depression levels based on current video data.

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Intelligent depression detection and support system: Statistical analysis, psychological review and design implication

Cited by 17 publications

References 7 publications

AI Based Mental Health Prediction System

AI Based Mental Health Prediction System

AI-Based Mental Health Prediction System

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

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