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Akademický článek

Enhanced Machine Learning Framework for Autonomous Depression Detection Using Modwave Cepstral Fusion and Stochastic Embedding

Autor: Jithin Jacob, K.S. Kannan

Publikováno v: Информатика и автоматизация, Vol 23, Iss 6, Pp 1754-1783 (2024)

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To addre

Externí odkaz: https://doaj.org/article/848eb638dfeb463388e4b6361ce3a18b

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Akademický článek

Depression recognition using voice-based pre-training model

Autor: Xiangsheng Huang, Fang Wang, Yuan Gao, Yilong Liao, Wenjing Zhang, Li Zhang, Zhenrong Xu

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Abstract The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset s

Externí odkaz: https://doaj.org/article/81d495a9cda74eafa45e74f4b8e90b5c

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Akademický článek

Automatic identification of depressive symptoms in college students: an application of deep learning-based CNN (Convolutional Neural Network)

Autor: He Tianqing, Huang Wei

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

Facial behavior is the most direct and easily accessible behavioral data. In this paper, based on the face action unit based on FACS, we have conducted quantitative research on the expression behavior pattern of depressed people with digital features

Externí odkaz: https://doaj.org/article/6d1342a78eee4536ab08912c83dd57cc

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Akademický článek

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Towards using Breathing Features for Multimodal Estimation of Depression Severity

Autor: Pessanha, Francisca, Kaya, Heysem, Akdag, Almila, Salah, Albert Ali, Sub Social and Affective Computing, Sub Human-Centered Computing, Social and Affective Computing

Publikováno v: 24th ACM International Conference on Multimodal Interaction, 128
STARTPAGE=128;TITLE=24th ACM International Conference on Multimodal Interaction

Breathing patterns are shown to have strong correlations with emotional states, and hence have promise for automatic mood order prediction and analysis. An essential challenge here is the lack of ground truth for breathing sounds, especially for medi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e91c375d3596098aab0984c3bce38b6
https://dspace.library.uu.nl/handle/1874/423741

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